Object Tracking (Computer Vision)

In this paper we describe a method of automatic motion tracking of a person in a sequence of video frames. The method is suitable for tracking a person walking or running in a surveillance video captured from a single still camera. The method initially removes noise from the captured images then segments the images using frame difference and binary conversion techniques and finally tracks the person using a bounding box based on occurrence of high intensity values horizontally and vertically. Simulation results show that the method can be used for real-time tracking of humans in videos with frame-rate of 25/30 fps. In the context of a Smart-city, the purposed system serves as a part of Internet of Things (IoT) and transmits the capture video to control-center for processing.

A research area in Computer Vision focuses on the identification of articulated objects, such as human actions and movements of the hand, which can be used in human-computer interaction, surveillance, and other tracking systems. Two problems arise: identify when two articulated objects in different stances are in the same class of objects, and differentiate the distinct positions of the same object. In both cases, it is necessary to know how correspond the different points or regions of such objects standing in different attitudes. This article presents the Contour-Point Signature; a point descriptor that allows to establish a method to achieve the better matching of points between two figures, and to thus obtain a transformation which relates them. With this descriptor, we can achieve more accurate shape features and implement more efficient retrieval under multi-resolution. In addition, CPS is robust to rigid translation, scaling, rotation and independent of the origin point. A measure of dissimilarity between two figures for classifying various human postures in a video sequence is also presented.

The investigation of 3D euclidean symmetry sets (SS) and medial axis is an important area, due in particular to their various important applications. The pre-symmetry set of a surface M in 3-space (resp. smooth closed curve in 2D) is the set of pairs of points which contribute to the symmetry set, that is, the closure of the set of pairs of distinct points p and q in M, for which there exists a sphere (resp. a circle) tangent to M at p and at q. The aim of this paper is to address problems related to the smoothness and the singularities of the pre-symmetry sets of 3D shapes. We show that the pre-symmetry set of a smooth surface in 3-space has locally the structure of the graph of a function from R^2 to R^2, in many cases of interest.

In this paper, we explore some of the applications of computer vision to sports analytics. Sport analytics deals with understanding and discovering patterns from a corpus of sports data. Analysing such data provides important performance metrics for the players, for instance in soccer matches, that could be useful for estimating their fitness and strengths. Team level statistics can also be estimated from such analysis. This paper mainly focuses on some the challenges and opportunities presented by sport video analysis in computer vision. Specifically, we use our multi-camera setup as a framework to discuss some of the real-life challenges for machine learning algorithms.

We would like to invite you to join this exciting new project as a chapter contributor. Since this is a textbook, a great deal of this chapter entails a survey on the topic under the paradigm of cyber-physical systems, what can be done onboard and remotely, the distributed nature of the system and some exercises on futurology (anticipating trends can shed some light on upcoming designs). IET will bring great visibility to your work. You are welcome to suggest another topic/chapter title if you feel it would be more suitable. Each chapter should be around 20-25 pages each and can be submitted as a Word or Latex File. The IET will send you additional information (formatting, permission form, etc.) with the contributor's agreement once you have agreed to contribute to the book. Visit http:// www.theiet.org/resources/author-hub/books/index.cfm to get all information you need as a contributor to an IET research-level book. Each book is expected to have a total number of 500 printed pages (based on approximately 550 words per page with a 20% allowance for figures and tables). We have included a tentative schedule and list of topics below. If this is something you would consider, please send me the title of your chapter, a short description/abstract of the chapter content, and your full contact details. We will expect original content and new results for this book. You can, of course, reuse published material but the percentage of material reuse for the chapter should be less than 40%. The IET will run a piracy software on the full manuscript to control that you are including original material and will reject chapters who contain a large amount of already-published material so please do take this into consideration.

It is well known that this soiling can reduce the generation efficiency in PV system. In some case according to the literature of loss of energy production in photovoltaic systems can reach up to 50%. In the industry there are various types of cleaning robots, they can substitute the human action, reducing cleaning cost, be used in places where access is difficult, and increasing significantly the gain of the systems. In this paper we present an application of computer vision method for soiling recognition in photovoltaic modules for autonomous cleaning robots. Our method extends classic CV algorithm such Region Growing and the Hough. Additionally, we adopt a pre-processing technique based on Top Hat and Edge detection filters. We have performed a set of experiments to test and validate this method. The article concludes that the developed method can bring more intelligence to photovoltaic cleaning robots.

The advancement of wireless sensor networks yields a variety of wireless sensor network for wildlife tracking. One typical application for wireless sensor networks is in animal tracking and monitoring in wildlife environments. A significant number of studies have been done in tracking animals with sensor networks. However from the recent literature it is observed that there is no much study has been done on an intelligent real time sensor network that is capable to alerting the rangers an incidence of animal poaching before it happened. In this paper an intelligent wireless sensor system for tracking and monitoring rhinos and elephants is proposed.

Multiple objects tracking finds its applications in many high level vision analysis like object behaviour interpretation and gait recognition. In this paper, a feature based method to track the multiple moving objects in surveillance video sequence is proposed. Object tracking is done by extracting the color and Hu moments features from the motion segmented object blob and establishing the association of objects in the successive frames of the video sequence based on Chi-Square dissimilarity measure and nearest neighbor classifier. The benchmark IEEE PETS and IEEE Change Detection datasets has been used to show the robustness of the proposed method. The proposed method is assessed quantitatively using the precision and recall accuracy metrics. Further, comparative evaluation with related works has been carried out to exhibit the efficacy of the proposed method.

Our aim is to estimate the perspective-effected geometric distortion of a scene from a video feed. In contrast to most related previous work, in this task we are constrained to using low-level, spatio-temporally local motion features only. This particular challenge arises in many semi-automatic surveillance systems which alert a human operator to potential abnormalities in the scene. Low-level, spatio-temporally local motion features are sparse (and thus require comparatively little storage space) and sufficiently powerful in the context of video abnormality detection to reduce the need for human intervention by more than 100-fold. This paper introduces three significant contributions: (i) we describe a dense algorithm for perspective estimation which uses motion features to estimate the perspective distortion at each image locus and then polls all such local estimates to arrive at the globally best estimate, (ii) we also present an alternative coarse algorithm which subdivides the image frame into blocks, and uses motion features to derive block-specific motion characteristics and constrain the relationships between these characteristics, with the perspective estimate emerging as a result of a global optimization scheme, and (iii) we report the results of an evaluation using nine large sets acquired using existing close-circuit television (CCTV) cameras, not installed specifically for the purposes of this work. Our findings demonstrate that both of the proposed methods are successful, their accuracy matching that of human labelling using complete visual data (by the constraints of the setup unavailable to our algorithms).

International Journal of Computer Vision and machine learning (IJCVML) is an open access, peer-reviewed journal that publishes articles which contribute new results in all areas of the advanced vision computing. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on understanding advances in vision computing and establishing new collaborations in these areas. Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of vision computing.

We present a visual object detector based on a deep convo-lutional neural network that quickly outputs bounding box hypotheses without a separate proposal generation stage [1]. We modify the network for better performance, specialize it for a robotic application involving "bird" and "nest" categories (including the creation of a new dataset for the latter), and extend it to enforce temporal continuity for tracking. The system exhibits very competitive detection accuracy and speed, as well as robust, high-speed tracking on several difficult sequences.

The field of tracking has seen a significant development in computer vision. Recognizing for object is essential part of a computer vision. The first step is detection toward tracking for moving object in video. The represent for object is the next step important in the tracking. The tracking for an object must be using monitoring this object temporal and spatial transforming in a sequence the video, with presence, shape, size, location and location etc. This paper provides a brief overview of the various detection, classification and tracking algorithms presented in the literature, containing comparative and analysis study of different techniques used for different stages.

As autonomous vehicles and autonomous racing rise in popularity, so does the need for faster and more accurate detectors. While our naked eyes are able to extract contextual information almost instantly, even from far away, image resolution and computational resources limitations
make detecting smaller objects (that is, objects that occupy a small pixel area in the input image) a truly challenging task for machines and a wide open research field.

This study explores ways in which the popular YOLOv5 object detector can be modified to improve its performance in detecting smaller objects, with a particular focus on its application to autonomous racing. To achieve this, we investigate how replacing certain structural elements of the
model (as well as their connections and other parameters) can affect performance and inference time. In doing so, we propose a series of models at different scales, which we name ‘YOLO-Z’, and which display an improvement of up to 6.9% in mAP when detecting smaller objects at 50% IOU, at a cost of just a 3ms increase in inference time compared to the original YOLOv5.

Our objective is not only to inform future research on the potential of adjusting a popular detector such as YOLOv5 to address specific tasks, but also to provide insights on how specific changes can impact small object detection. Such findings, applied to the wider context of autonomous vehicles, could increase the amount of contextual information available to such systems.

Content based image retrieval (CBIR), also known as query by image content (QBIC) is the application of computer vision to the image retrieval problem, that is, the problem of searching for digital images in large databases. Users of image databases often prefer to
retrieve relevant images by categories. Unfortunately, images are usually indexed by content or low level features like color, texture and shape, which often fail to capture high level concepts well. Again the high level concepts or semantics can vary from user to user. To address these issues, relevance feedback has been extensively used to associate low level image features with high level concepts. Because it is hard to define what will be the most similar images of a query image as it varies from a user to another, taking user judgement on the retrieved images and then refining the search result based on user’s region of interest has become very successful. Many researchers have addressed the issues of Content based Image retrieval with relevance feedback and contributed in this field to a considerable extent. Still there are scope and need of much improvement. Among all existing relevance feedback approaches, query point movement and feature re-weighting have been proven to be suitable for large-scaled image databases with high dimensional image features. In this thesis, we present a query point movement approach using both relevant and irrelevant images to catch the user semantics. The relevance feedback strategy is simple and straightforward. The basic approach adopted here is to move the query point to the direction of user intention. The user only selects the images that he/she thinks relevant and
ignores the irrelevant ones. The feedback provided by the user are categorized into positive and negatives images both of which categories are used in determining the query point for further iterations. During experimentation it is observed that our system can efficiently move towards the user context with minimum possible number of iterations comparing to trivial methods.

Deep learning and image processing are two areas of great interest to academics and industry professionals alike. The areas of application of these two disciplines range widely, encompassing fields such as medicine, robotics, and security and surveillance. The aim of this book, 'Deep Learning for Image Processing Applications', is to offer concepts from these two areas in the same platform, and the book brings together the shared ideas of professionals from academia and research about problems and solutions relating to the multifaceted aspects of the two disciplines. The first chapter provides an introduction to deep learning, and serves as the basis for much of what follows in the subsequent chapters, which cover subjects including: the application of deep neural networks for image classification; hand gesture recognition in robotics; deep learning techniques for image retrieval; disease detection using deep learning techniques; and the comparative analysis of deep data and big data. The book will be of interest to all those whose work involves the use of deep learning and image processing techniques

A crucial prerequisite for recycling forming an integral part of municipal solid waste (MSW) management is sorting of useful materials from source-separated MSW. Researchers have been exploring automated sorting techniques to improve the overall efficiency of recycling process. This paper reviews recent advances in physical processes, sensors, and actuators used as well as control and autonomy related issues in the area of automated sorting and recycling of source-separated MSW. We believe that this paper will provide a comprehensive overview of the state of the art and will help future system designers in the area. In this paper, we also present research challenges in the field of automated waste sorting and recycling.

In the present context, there is one big issue regarding energy conservation. In fact, it is a
valid question-why do we need to conserve energy? It is not at all wrong if the answer would
be to save money. But certainly, it is not the only reason. Less electricity uses leads to less
burning of fossil fuels. Although it is possible to generate electricity from the renewable
and clean natural resources like water, wind, and sun, most of the electricity is generated by
burning fossil fuels like coals and oil which are non-renewable natural resources and cannot
be replenished once they get used up. They are not clean sources of energy either and are
responsible for emitting greenhouse gases as well as causing pollution. Energy conservation
also helps to prevent the threats to the ecosystem caused during procurement of coal and oil
spills. There is an effort to reduce fossil fuel consumption by the means of nuclear reaction.
Though it can generate a large amount of electricity, yet it is not the best solution as
it produces radioactive waste products. Thus, decreasing the demand for electricity is very
essential.
To increase energy efficiency, the home automation system can play a significant role. The
lighting system, as well as other electric appliances, could be made to automatically turn off
whenever they are not in use even if the user forgets to switch them off. Earlier such systems
have been implemented using various approaches in which specific sensor is used to measure
a specific parameter. Use of those sensors make the circuit more complex and it may always
not be reliable.
This led to the idea of using a camera as a sensor. Using video surveillance, the changes in
the captured area is measured as a result of which we can perform the desired automation.
It is much reliable than the use of other sensors. Computer vision has made it possible to
acquire, process, analyze and extract high-level understanding for digital images and videos.
Computers can capture and process the images with details far better than human beings.
In this study, YOLOv3 is implemented as an object detection system for human detection
and finding if the appliances are on or off. This system replaces the use of predominant sensors
by computer vision. With the advanced tracking by detection technique- Deep SORT,
the movement of human beings is constantly tracked in order to increase the reliability of the
system. Also, the modification of person re-identification model is presented to improve the
performance of Deep SORT tracking.

Current multi-person localisation and tracking systems have an over reliance on the use of appearance models for target re-identification and almost no approaches employ a complete deep learning solution for both objectives. We present a novel, complete deep learning framework for multi-person localisation and tracking. In this context we first introduce a light weight sequential Generative Adversarial Network architecture for person localisation, which overcomes issues related to occlusions and noisy detections, typically found in a multi person environment. In the proposed tracking framework we build upon recent advances in pedestrian trajectory prediction approaches and propose a novel data association scheme based on predicted trajectories. This removes the need for computationally expensive person re-identification systems based on appearance features and generates human like trajectories with minimal fragmentation. The proposed method is evaluated on multiple public benchmarks including both static and dynamic cameras and is capable of generating outstanding performance, especially among other recently proposed deep neural network based approaches.

This thesis aims to develop a monocular vision system to track an Unmanned Aerial Vehicle (UAV) pose (3D position and orientation) relative to the camera reference frame during its landing on a ship. The vast majority of accidents and incidents occur during take-off or landing since, in the vast majority of systems, an external pilot takes control. Having less human intervention increases system reliability and alleviates the use of certified pilots. Due to UAV size and weight, take-off is easily performed by hand, so the main focus will be in the landing maneuver. The vision system is located on the ship’s deck, which reduces demands on the UAV’s processing power, size, and weight. The proposed architecture is based on an Unscented Particle Filter (UPF) scheme with two stages: (i) pose boosting, and (ii) tracking. In the pose boosting stage, we detect the UAV on the captured frame using Deep Neural Networks (DNNs) and initialize a set of pose hypotheses that are likely to describe the true pose of the target using a pre-trained database indexed by bounding boxes. In the tracking stage, we use a UPF based approach to obtain an online estimate of the true pose of the target. On contrary to many vision-based particle filters that sample particles from a distribution that is based solely on predictions from the previous frames, in this work, we also use information from the current frame to improve the convergence of the filter. We fuse information from current and previous time steps with Unscented Transform (UT) filters, and use, for the first time in this type of problem, the Bingham and Bingham-Gauss distributions to model the dynamics and noise of the orientation in its natural manifold. These filters depend on the computation of importance weights that use sub-optimal approximations to the likelihood function. We evaluate different similarity metrics that compute a distance measure between an artificial rendered image with the hypothetic state of the system and the captured frame. Since we are approximating the likelihood function, we enrich the filter with additional refinement steps to abridge its sub-optimality. We have developed a “realistic” simulator for a quantitative analysis of the results. The entire description and experimental analysis of the system is based on the tracking error and processing time. When analyzing a landing sequence with a real sky gradient filled with clouds, we have obtained approximately 81% less rotation error using the Unscented Bingham Filter (UBiF) and the Unscented Bingham-Gauss Filter (UBiGaF) when compared to the simple Unscented Kalman Filter (UKF) without considering the use of pose optimization. When we use pose optimization, we can decrease the obtained rotation error in more than 50%.

Overview of ongoing research in the CISA group (WISE research lab) at the Vrije Universiteit Brussel. The group is investigating cross-media information spaces and architectures, interactive paper and augmented reality solutions as well as multimodal and multi-touch interaction frameworks. Thereby, we are doing research on new document formats for representing information across different types of media as well as fluid user interfaces for interacting with the resulting cross-media information spaces. A significant part of our research is based on applying and extending the resource-selector-link (RSL) hypermedia metamodel.

More information: https://beatsigner.com/cross-media-information-spaces-and-architectures.html

Modern Unmanned Aircraft Systems (UAS) employ a variety of sensors and multi-sensor data fusion techniques to provide advanced operational capabilities and trusted autonomy (i.e., required levels of safety, integrity, security and interoperability), when sharing the airspace with other manned and unmanned platforms. Low-cost and high-performance Navigation and Guidance Systems (NGS) for UAS have been developed at RMIT University by employing a combination of compact and lightweight sensors to satisfy the Required Navigation Performance (RNP) in all flight phases. Additionally, recent research at RMIT has focused on the development of a unified approach to separation assurance and collision avoidance suitable for UAS cooperative/non-cooperative sensor architectures and allowing for an extended range of operations both in mission-essential and safety-critical tasks. The Sense-and-Avoid Unified Method (SUM) developed at RMIT provides an innovative analytical framework to combine real-time measurements (and associated uncertainties) of navigation states, platform dynamics and tracking observables to produce high-fidelity dynamic geo-fences suitable for integration in future avionics, Air Traffic Management (ATM) and defense decision support tools.

In this thesis we introduce a Computer Vision based traffic light recognition system for intelligent vehicles. The system is divided into three stages: in the first phase we use color learning, morphology and contextual information for traffic light lamp detection in order to generate candidates.
After that, in the second phase we use SVM classifier plus candidate generation from the previous detections. finally the third phase is dedicated to tracking using a simple method based in dense candidate generation of previous frame detections. To compare our image processing algorithm with the state of the art methods we create a labeling tool in order to label each candidate as sign or not sign and standardize available traffic light datasets. In addition, we implement two state of the art traffic lights recognition systems. The extended comparison gives an useful information to future traffic light recognition systems an at the same time a list of future work in order to improve the obtained results. The experimental results demonstrate that our system has better results evaluated on the avaliable dataset than the others having yet many possibilities of improvement.

Visual tracking of unknown objects in unconstrained video-sequences is extremely
challenging due to a number of unsolved issues. This thesis explores several of these
and examines possible approaches to tackle them.

The unconstrained nature of real-world input sequences creates huge variation in
the appearance of the target object due to changes in pose and lighting. Additionally,
the object can be occluded by either parts of itself, other elements of the scene, or
the frame boundaries. Observations may also be corrupted due to low resolution,
motion blur, large frame-to-frame displacement, or incorrect exposure or focus of the
camera. Finally, some objects are inherently difficult to track due to their (low) texture,
specular/transparent nature, non-rigid deformations, etc.

Conventional trackers depend heavily on the texture of the target. This causes issues
with transparent or untextured objects. Edge points can be used in cases where
standard feature points are scarce; these however suffer from the aperture problem. To
address this, the first contribution of this thesis explores the idea of virtual corners,
using pairs of non-adjacent line correspondences, tangent to edges in the image. Furthermore,
the chapter investigates the possibility of long-term tracking, introducing a
re-detection scheme to handle occlusions while limiting drift of the object model. The
outcome of this research is an edge-based tracker, able to track in scenarios including
untextured objects, full occlusions and significant length. The tracker, besides reporting
excellent results in standard benchmarks, is demonstrated to successfully track the
longest sequence published to date.

Some of the issues in visual tracking are caused by suboptimal utilisation of the
image information. The object of interest can easily occupy as few as ten or even one
percent of the video frame area. This causes difficulties in challenging scenarios such
as sudden camera shakes or full occlusions. To improve tracking in such cases, the next
major contribution of this thesis explores relationships within the context of visual
tracking, with a focus on causality. These include causal links between the tracked
object and other elements of the scene such as the camera motion or other objects.
Properties of such relationships are identified in a framework based on information
theory. The resulting technique can be employed as a causality-based motion model to
improve the results of virtually any tracker.

Significant effort has previously been devoted to rapid learning of object properties
on the fly. However, state-of-the-art approaches still often fail in cases such as
rapid out-of-plane rotations, when the appearance changes suddenly. One of the major
contributions of this thesis is a radical rethinking of the traditional wisdom of modelling
3D motion as appearance change. Instead, 3D motion is modelled as 3D motion.
This intuitive but previously unexplored approach provides new possibilities in visual
tracking research.

Firstly, 3D tracking is more general, as large out-of-plane motion is often fatal for 2D
trackers, but helps 3D trackers to build better models. Secondly, the tracker's internal
model of the object can be used in many different applications and it could even become
the main motivation, with tracking supporting reconstruction rather than vice versa.
This effectively bridges the gap between visual tracking and Structure from Motion.
The proposed method is capable of successfully tracking sequences with extreme out-of-plane
rotation, which poses a considerable challenge to 2D trackers. This is done by creating
realistic 3D models of the targets, which then aid in tracking.

In the majority of the thesis, the assumption is made that the target's 3D shape is
rigid. This is, however, a relatively strong limitation. In the final chapter, tracking and
dense modelling of non-rigid targets is explored, demonstrating results in even more
generic (and therefore challenging) scenarios. This final advancement truly generalises
the tracking problem with support for long-term tracking of low texture and non-rigid
objects in sequences with camera shake, shot cuts and significant rotation.

Taken together, these contributions address some of the major sources of failure
in visual tracking. The presented research advances the field of visual tracking, facilitating
tracking in scenarios which were previously infeasible. Excellent results are
demonstrated in these challenging scenarios. Finally, this thesis demonstrates that 3D
reconstruction and visual tracking can be used together to tackle difficult tasks.

I. Latar Belakang
Pengenalan industri merupaka kegiatan yang dilakukan diluar kampus untuk menambah pengetahuan mahasiswa tentang dunia kerja. Sehingga mahasiswa dapat memperoleh pengetahuan mengenai proses produksi dalam industri. Selain itu mahasiswa diharapkan dapat mengali informasi tentang proses dan tahapan produksi.  Pada kesempatan ini mahasiswa D3 Teknik Kimia melakukan pengenalan insdustri di PT Madubaru (PG/PS Madukismo). Pengenalan industri dilakukan untuk memberikan bayangan awal kepada mahasiswa terhadap dunia industri dan  proses produksi yang berlangsung di dalamnya. Sehingga mahasiswa dapat mengaplikasikan ilmu yang didapat di kampus pada saat bekerja nantinya. Kemudian mahasiswa diwajibkan untuk membuat laporan atas informasi yang telah didapatkan saat pengenalan industri yang berkaitan dengan perusahaan yang bersangkutan.

The camera tracking is particularly useful for Virtual and Augmented Reality systems because tracked objects may serve as interaction devices in 3D scenes. Real object representations are built from the optical tracking of real object projections on photosensor arrays. Cameras as tracking devices have the advantage of not having mechanical contact. However, their effective application requires the knowledge of the main parameters and features involved in the tracking process. This chapter is a very short ...

Ground station antennas are a part of TTC system, generally, Yagi-Udi antennas and Parabola dish antenna are using in Earth segment to communication with LEO small satellites, this paper uniquely presents the three huge antennas of Beihang University ground station which are communicating with some microsatellites with view window above Beijing, China. The ground station contains two Yagi-Udi antennas for VHF/UHF and an S-band dish antenna for reception of payloads data. For verification feasibility of the antennas, simulations have been accomplished according to the antennas requirements. Eventually, the simulations assisted to recognize the matched commercial ground station antennas based on comparison of the simulations with commercial antennas and the matched ones are chosen for the implementation of Beihang University ground station antennas.

— In this paper, we propose a new tractable Bernoulli filter based on the random matrix framework to track an extended target in an ultra-wideband (UWB) sensor network. The resulting filter jointly tracks the kinematic and shape parameters of the target and is called the extended target Gaussian inverse Wishart Bernoulli (ET-GIW-Ber) filter. Closed form expressions for the ET-GIW-Ber filter recursions are presented. A clustering step is inserted into the measurement update stage in order to have a computationally tractable filter. In addition, a new method that is consistent with the applied clustering method is embedded into the filter recursions in order to adaptively estimate the time-varying number of measurements of the extended target. The simulation results demonstrate the robust and effective performance of the proposed filter. Furthermore, real data collected from a UWB sensor network are used to assess the performance of the proposed filter. It is shown that the proposed filter yields a very promising performance in estimation of the kinematic and shape parameters of the target.

Pembuatan Akta di Notaris merupakan salah satu proses yang sangat panjang, dan
untuk menunggu proses itu dibutuhkan waktu yang lama untuk mencari tahu sampai dimana
proses itu di buat. Pada umumnya proses pembuatan Akta baru dilakukan melalui tahapan
pendaftaran, persyaratan, pembayaran, penandatanganan dan pengecekan. Pada penerapan di
Kantor Notaris SISCA ANGELIA WULAN SARI,S.H.,M.Kn. Galuh Mas Karawang, yang
selama ini dilakukan secara manual seperti datang ke kantor notaris untuk pembuatan Akta
dan harus mendaftar terlebih dahulu setelah itu memberikan persyaratan memberi tanda
terima dan menginformasikannya mendatangi kantor untuk bertemu karyawan notaris atau
melalui telepon, yang memungkinkan masih banyak kekurangan, menginformasikan melalui
mendatangi kantor untuk bertemu karyawan notaris atau melalui telepon membutuhkan
waktu lagi dan membuang waktu dengan sia-sia. Untuk itu dibutuhkan suatu Pengembangan
Real Time Tracking System Pembuatan Akta Berbasis Web yang dapat membantu dalam
proses pembuatan akta baru. Rumusan masalah dari penelitian ini adalah bagaimana proses
membangun Pengembangan  Real Time Tracking System Pembuatan Akta Berbasis Web di
Kantor Notaris Sisca Angelia Wulan Sari, S.H., M.Kn. Galuh Mas Karawang, sehingga dapat
menyajikan pengembangan yang akurat serta efisien. Adapun tujuan dari penelitian ini adalah
menghasilkan suatu program sistem aplikasi. Manfaat dari penelitian ini adalah memberikan
kemudahan bagi Karyawan Notaris dan Client itu sendiri. Metode penelitian yang digunakan
adalah pengumpulan data, analisis, perancangan, uji coba dan implementasi. Kesimpulan
hasil dari penelitian ini adalah diharapkan dapat terbangunnya Pengembangan  Real Time
Tracking System Pembuatan Akta Berbasis Web  di Kantor Notaris Sisca Angelia Wulan Sari,
S.H., M.Kn.

High Speed, Modular and Reconfigurable Object Tracking Module: Cuboid Object Tracking Module COTM is an object tracking module that supports intuitive interfaces (like speech and sound based tracking), swarm planning and tracking (for multiple robots tracking same object), modular design (same electronics being reused for different robots for all functionalities excepting the actuation), reconfigurability on-the-go, High speed tracking (with maximum speed of 20000 kilometers per hour or orbital velocity of planet/satellite whichever is lesser) and GCS independent control when necessary.

Patent Journal:    http://www.ipindia.nic.in/writereaddata/Portal/IPOJournal/1_4789_1/Part-1.pdf
Application number: 201841035016
Journal number:    38/2019
Journal date:      20/09/2019