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In order to reduce the processing volume of multi-camera real-time video data in smart city scenarios, a video collaborative analysis method based on machine learning algorithms at the edge was proposed.Firstly, for the important objects detected by each camera, different key windows were designed to filter the region of interest (RoI) in the video, reduce the video data volume and extract its features.Then, based on the extracted data features, the same objects in the videos from different cameras were annotated, and a strategy for calculating the association degree value between cameras was designed for further reducing the video data volume.Finally, the GC-ReID algorithm based on graph convolutional network (GCN) and re-identification (ReID) was proposed, aiming at achieving the collaborative analysis of multi-camera videos.The experimental results show that proposed method can effectively reduce the system latency and improve the video compression rate while ensuring the high accuracy, compared with the existing video analysis methods.

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