Performance analysis of object detection and tracking methodology for video synopsis
by Swati Jagtap; Nilkanth Chopade
International Journal of Signal and Imaging Systems Engineering (IJSISE), Vol. 13, No. 1, 2024

Abstract: The enormous amount of data produced by 24/7 surveillance cameras is challenging for retrieval and browsing of video. The challenges can be overcome by reducing the video size through video condensation methods without affecting the information. Video synopsis is a condensation technique where the long video is represented in shorter form by reducing the spatial and temporal redundancy based upon the occurrence of activity that eases the video browsing and retrieval. The detection and tracking an object in a surveillance camera are essential steps in video synopsis. The proposed research compares different detection and tracking algorithms used as a first stage for video synopsis. The condensation ratio get affected due to improper detection and tracking algorithm selection. Based on evaluating both quantitative and qualitative parameters, the You Only Look Once version 4 (YOLOV4) network outperforms the Gaussian mixture model (GMM) and SSDMobileNet in detecting multiple objects within video surveillance datasets. This research will be helpful to the researcher in identifying the correct pre-processing steps in the domain of video synopsis. In future research, incorporating an auto-learning anchor model could significantly enhance accuracy.

Online publication date: Mon, 15-Jul-2024

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