Abstract
The paper proposed a framework to design and develop object detection and tracking using learning based algorithms. It is an important event in security and surveillance systems using images or videos. YOLO V3 model is used for object detection and is trained using coco dataset. The objects detected are Humans, bottles, Drilling machine, Air powered saw, etc. The model detect the objects from camera field view, i.e. objects that are clearly viewed. The proposed model with pre-processing, provides an average accuracy of around 96% with different test cases.
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Burde, S., Budihal, S.V. (2021). Multiple Object Detection and Tracking Using Deep Learning. In: Sabut, S.K., Ray, A.K., Pati, B., Acharya, U.R. (eds) Proceedings of International Conference on Communication, Circuits, and Systems. Lecture Notes in Electrical Engineering, vol 728. Springer, Singapore. https://doi.org/10.1007/978-981-33-4866-0_32
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DOI: https://doi.org/10.1007/978-981-33-4866-0_32
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