Object Tracking: An Experimental and Comprehensive Study on Vehicle Object in Video

Object Tracking: An Experimental and Comprehensive Study on Vehicle Object in Video

Автор: Vo Hoai Viet, Huynh Nhat Duy

Журнал: International Journal of Image, Graphics and Signal Processing @ijigsp

Статья в выпуске: 1 vol.14, 2022 года.

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Tracking objects on camera or video is very important for automated surveillance systems. Along with the development of techniques and scientific research in object tracking, automatic surveillance systems have gradually become better. With the input of a frame including the object to be tracked and the location information of the object to be tracked in that video. The output will be the prediction of the position of the object to be tracked on the next frame. This paper presents the comparison and experiment of some traditional object tracking methods and suggestions for improvement between them. Firstly, we examined related studies, traditional object tracking models. Secondly, we examined image and video data sets for verification purposes. Thirdly, experimenting with some related research works in traditional object tracking problems, evaluation of the existing model, what has been achieved and what has not been achieved for the current models. Propose improvements based on the combination of traditional methods. Finally, we aggregate these results to evaluate for each type of object tracking model. The results show that Particles Filter method has the highest CDT with TO score of 0.907971 on VOT dataset and 0.866259 on UAV123 dataset. However, the most stable are the two hybrid methods, the Particle filter base on Mean shift method has a TF score of 31.1 on the VOT dataset and the Kalman Filter base on Mean shift method has a TME score of 28.8233 on the UAV dataset. Because low-level features cannot represent all the information of an object to be tracked during the completion of the experiment, we can conclude that combining deep learning network and using high-level feature into the tracking model can bring better performance in the future.

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Object Tracking, Surveillance Systems, Single-Object Tracking, Mean-Shift, Kalman Filter, Particle Filter

Короткий адрес: https://sciup.org/15018306

IDR: 15018306   |   DOI: 10.5815/ijigsp.2022.01.06

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