Abstract
Aiming at the fixed-view video surveillance scene, this paper proposes a video object detection method that combines motion features and YOLO. The method uses the method of filtering video frames without motion features and segmenting video frames with motion features to reduce the reasoning pressure of the YOLO algorithm model. In this process, video frames containing moving objects are first obtained by the moving object detection module. Second, the moving target will be recognized by the object of interest recognition module. Finally, the background decision module records and analyzes the detection results to obtain background model updates or result output. It detects moving objects without using traditional background modeling methods. Experiments based on the CDnet2014 dataset show that our method improves the missed detection rate by 0.098% and the average inference speed per frame by 45.62% compared with the YOLO-based humanoid detection method. Furthermore, the method has superior performance in scenarios where target objects appear less frequently (substations, transmission lines, and hazardous areas).
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Xiao, C. et al. (2022). Research on Video Object Detection Methods Based on YOLO with Motion Features. In: Wang, Y., Zhu, G., Han, Q., Wang, H., Song, X., Lu, Z. (eds) Data Science. ICPCSEE 2022. Communications in Computer and Information Science, vol 1628. Springer, Singapore. https://doi.org/10.1007/978-981-19-5194-7_27
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DOI: https://doi.org/10.1007/978-981-19-5194-7_27
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