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20 November 2019 A fast moving object recognition method for video surveillance system
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Prevailing object detection algorithms such as RCNN, YOLO and SSD usually are not suitable for high definition surveillance systems because of the fixed size of network input and masses of object candidate regions in the select search process. This paper proposes a fast moving object detection and recognition method for video surveillance system, which applies background extraction and frame difference to fulfill select search process, followed by a pretrained CNN model inference to complete object recognition. Proposed method was proved to be fast and effective in our experimental results which is more suitable for moving object detection for video surveillance system, compared to current other object detection algorithms.
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Bo Lei, Qiang Fan, and Hai Tan "A fast moving object recognition method for video surveillance system", Proc. SPIE 11192, Real-time Photonic Measurements, Data Management, and Processing IV, 1119211 (20 November 2019);

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