Camera Calibration

Vision-based automatic traffic monitoring systems require a calibrated camera in order to measure the speeds of tracked vehicles. Typically this calibration is done by hand. We present an automatic technique to calibrate the camera for typical viewpoints on highways using a realtime boosted cascade vehicle detector (BCVD). Image processing is used to estimate the two vanishing points, from which the camera height, focal length, and pan and tilt angles are calculated. The key contribution of the proposed approach is its applicability to a wide variety of environmental conditions. The technique does not rely upon background subtraction, nor does it require scene-specific features such as pavement markings. As a result, it is unaffected by the presence of shadows, adverse weather conditions, headlight reflections, lack of ambient light, or spillover caused by low-mounted cameras. Speed estimation within 10% of ground truth is shown for sequences obtained during daylight, nighttime, and rain, and including shadows and severe occlusion.

3DTV production of live sports events presents a challenging problem involving conflicting requirements of maintaining broadcast stereo picture quality with practical problems in developing robust systems for cost effective deployment. In this paper we propose an alternative approach to stereo production in sports events using the conventional monocular broadcast cameras for 3D reconstruction of the event and subsequent stereo rendering. This approach has the potential advantage over stereo camera rigs of recovering full scene depth, allowing inter-ocular distance and convergence to be adapted according to the requirements of the target display and enabling stereo coverage from both existing and 'virtual' camera positions without additional cameras. A prototype system is presented with results of sports TV production trials for rendering of stereo and free-viewpoint video sequences of soccer and rugby.

Bitcoin is currently the leading global provider of cryptocurrency which has recently received a lot of attention from the public. In while facilitating services and product deals. The factors that affect the Bitcoin price and the patterns behind its fluctuations can be predicted by using various mach This prediction may give better insights about the bitcoin price to the people who are investing in the bitcoin. Accurate prediction of the future trend in the closed price of the day for a given Machine Learning models can be used to predict the closed price for a given cryptocurrency Keywords: prediction bitcoin price calibration In today's era, the planet is digitalizing. there's a Cryptocurrency is digital cash that's very fashionable. The cryptocurrency may be a money primarily managed or exchanged on computer systems. Cryptocurrencies use blockchai are highly secure and transparent. So, cryptocurrencies can become a replacement sort of taking advantage of the longer term. There are more than 900 cryptocurrencies currently available to take a position online and this number is consistently growing. of those cryptocurrencies, undoubtedly the foremost popular has been Bitcoin and it had been also the primary cryptocurrency within the market. People can make use of bitcoin rather than cash. A. Literature Survey This section overlooks similar existing solutions and examines the algorithms used and drawbacks. Enhancing Bitcoin Price Fluctuation Using Attentive LSTM and Embedding network [1] study used traditional machine learning including Random Forest, XGBoost, S machine learning methods cannot capture the time dependency of time series. Recently, deep learning methods such as Recurrent Neural Networks (RNN) can handle the issue of time depend out long-term dependencies foremost commonly used variants of RNN can solve the vanishing gradient problem. long-term dependencies due to ARIMA [2] analyses neural network autoregression (NNA Bitcoin price forecast results, considering two different training and test samples. I better than ARIMA, while ARIMA outperforms NNAR in the second training

—In the traditional camera calibration method, one of the core parameters affecting the calibration quality is the mapping matrices in the x-and y-direction. In this study, we first calculate the mapping matrices using the traditional method. And then adopt Particle Swamp Optimization (PSO) method to further fine tune the mapping matrices. In the experiments, we use OpenCV packages to perform the original calibration algorithm, and use Python to implement the PSO algorithm to fine tune the mapping matrices. Experimental results show that, we have only minor improvement in terms of re-projection error (since this quantity is coarse), but we do produce better vision effects, especially for the regions distant from the image center.

We have developed an app, named Sunpass, able to convert every smartphone into a solar compass. Sunpass uses input data from the smartphone sensors, calculates the Sun position, and elaborates data to give the desired information.
The azimuth values measured by a smartphone equipped with Sunpass show a typical accuracy of 0.5°, which is limited by camera aberrations and misalignment of both accelerometer and CCD camera of the smartphone.
In this paper, we show that both accuracy and reliability in azimuth measurements can be improved by a specific calibration procedure and a dedicated mechanical tool. We obtained a remarkable accuracy better than 0.06° on the single azimuth measurements, which improves to 0.03° on the average of eight measurements.

Development of the technology in the area of the cameras, computers and algorithms for 3D the reconstruction of the objects from the images resulted in the increased popularity of the photogrammetry. Algorithms for the 3D model reconstruction are so advanced that almost anyone can make a 3D model of photographed object. The main goal of this paper is to examine the possibility of obtaining 3D data for the purposes of the close-range photogrammetry applications, based on the open source technologies. All steps of obtaining 3D point cloud are covered in this paper. Special attention is given to the camera calibration, for which two-step process of calibration is used. Both, presented algorithm and accuracy of the point cloud are tested by calculating the spatial difference between referent and produced point clouds. During algorithm testing, robustness and swiftness of obtaining 3D data is noted, and certainly usage of this and similar algorithms has a lot of potential in the real-time application. That is the reason why this research can find its application in the architecture, spatial planning, protection of cultural heritage, forensic, mechanical engineering, traffic management, medicine and other sciences.