Abstract
We propose a CNN-based framework for “real-time object detection and tracking using deep learning” in this paper, which includes a spatial–temporal mechanism. The impact of efficient data on performance benchmarks in terms of accuracy has changed. The data processing is handled by industry buzzwords: deep learning (DL) and computer vision (CV). The CNN-based framework uses the single object tracker value to match arrival models and find targets in the next frame. Simply applying single object tracking to multiple object tracking will encounter problems in computational efficiency and results due to occlusion. In this paper, we introduce a “spatial attention mechanism (STAM)” to manage occlusion bias and target interaction. Object tracking is a sensational technology in image processing with great future implications. Multiple object tracking (MOT) has seen an extensive boom in the last few years due to machine learning, deep learning, computer vision, and more. This paper aims to provide an object tracking software solution. Using YOLO’s “You Only Look Once” technology with the help of Tensor flow, the system is geared toward object detection, tracking, and counting. Proven, effective detection and tracking on various dataset. Algorithms that offer real-time, accurate, and precise identifications appropriate for real-time applications.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Nguyen VD et al (2017) Learning framework for robust obstacle detection, recognition, and tracking. IEEE Trans Intell Transport Syst 18(6):1633–1646
Kain Z et all (2018) Detecting abnormal events in university areas. In: 2018 International conference on computer and applications (ICCA), pp 260–264
Wang P et al (2018) Detection of unwanted traffic congestion based on existing surveillance system using in freeway via a CNN-architecture trafficnet. In: IEEE conference on industrial electronics and applications (ICIEA), Wuhan, 2018, pp 1134–1139
Mu Q, Wei Y, Liu Y, Li Z (2018) The research of target tracking algorithm based on an improved PCANet. In: 10th international conference on intelligent human-machine systems and cybernetics (IHMSC), Hangzhou, 2018, pp 195–199
Baykara HC et al (2017) Real-time detection, tracking and classification of multiple moving objects in UAV videos. In: 29th IEEE international conference on tools with artificial intelligence (ICTAI), Boston, MA, 2017, pp 945–950
Wang W, Shi M, Li W (2017) Object tracking with shallow convolution feature. In: 9th international conference on intelligent human-machine systems and cybernetics (IHMSC), Hangzhou, 2017, pp 97–100
Muhammad K et al (2018) Convolutional neural networks based fire detection in surveillance videos. IEEE Access 6:18174–18183
Hernandez DE et al (2018) Cell tracking with deep learning and the Viterbi algorithm. In: International conference on manipulation, automation and robotics at small scales (MARSS), Nagoya, 2018, pp 1–6
Qian X et al (2017) An object tracking method using deep learning and adaptive particle filter for night fusion image. In: 2017 International conference on progress in informatics and computing (PIC), Nanjing, 2017, pp 138–142
Yoon Y et al (2018) Online multi-object tracking using selective deep appearance matching. In: IEEE international conference on consumer electronics—Asia (ICCE-Asia), Jeju, pp 206–212
Bharadwaj HS, Biswas S, Ramakrishnan KR (2016) A large scale dataset for classification of vehicles in urban traffic scenes. In: Proceedings of the 10th Indian conference on computer vision, graphics and image processing, ACM
Mohana et al, Performance evaluation of background modeling methods for object detection and tracking. In: International conference on inventive systems and control (ICISC)
Chandan G et al (2018) Real time object detection and tracking using deep learning and OpenCV. In: International conference on inventive research in computing applications (ICIRCA)
Mohana et al, Elegant and efficient algorithms for real time object detection, counting and classification for video surveillance applications from single fixed camera. In: International conference on circuits, controls, communications and computing (I4C)
Mohana et al, Simulation of object detection algorithms for video surveillance applications. In: 2nd international conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)
Raghunandan A et al,Object detection algorithms for video surveillance applications. In: International conference on communication and signal processing (ICCSP)
Mangawati A et al, Object tracking algorithms for video surveillance applications. In: 2018 international conference on communication and signal processing (ICCSP)
Mohana et al, Design and implementation of object detection, tracking, counting and classification algorithms using artificial intelligence for automated video surveillance applications. In: Advanced computing and communication society (ACCS)—24th annual international conference on advanced computing and communications (ADCOM-2018), IIITB, Bangalore
Jo KU, Im JH, Kim J, Kim DS (2017) A real-time multi-class multi-object tracker using YOLOv2. In: IEEE ICSIPA, Malaysia, September 12–14
S Sanjana VR Shriya G Vaishnavi K Ashwini 2021 A review on various methodologies used for vehicle classification, helmet detection and number plate recognition Evol Intel 14 2 979 987
Kusuma T, Ashwini K (2021) Modular ST-MRF environment for moving target detection and tracking under adverse local conditions. In: International conference on big data analytics. Springer, Cham, pp 93–105
Kusuma T, Ashwini K (2018) Real time object tracking in H. 264/AVC using polar vector median and block coding modes. Int J Comp Inform Eng 12(11):981–985
Kodipalli A, Devi S (2021) Prediction of PCOS and mental health using fuzzy inference and SVM. Frontiers in Public Health
Kusuma T, Ashwini K (2022) Analysis of deep learning frameworks for object detection in motion. Int J Knowl Based Intell Eng Syst. ISSN:1327-2314. https://doi.org/10.3233/kes-220002
Kusuma T, Ashwini K (2022) Multiple object tracking using STMRF and YOLOv4 deep SORT in surveillance video. Int J Res Trends Innov. ISSN:2456-3315
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Kusuma, T., Ashwini, K. (2023). Real-Time Object Detection and Tracking Design Using Deep Learning with Spatial–Temporal Mechanism for Video Surveillance Applications. In: Saini, H.S., Sayal, R., Govardhan, A., Buyya, R. (eds) Innovations in Computer Science and Engineering. ICICSE 2022. Lecture Notes in Networks and Systems, vol 565. Springer, Singapore. https://doi.org/10.1007/978-981-19-7455-7_56
Download citation
DOI: https://doi.org/10.1007/978-981-19-7455-7_56
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-7454-0
Online ISBN: 978-981-19-7455-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)