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With the rapid development of intelligent transportation system applications,a tremendous amount of multi-view video data has emerged to enhance vehicleperception. However, performing video analytics efficiently by exploiting thespatial-temporal redundancy from video data remains challenging. Accordingly,we propose a novel traffic-related framework named CEVAS to achieve efficientobject detection using multi-view video data. Briefly, a fine-grained inputfiltering policy is introduced to produce a reasonable region of interest fromthe captured images. Also, we design a sharing object manager to manage theinformation of objects with spatial redundancy and share their results withother vehicles. We further derive a content-aware model selection policy toselect detection methods adaptively. Experimental results show that ourframework significantly reduces response latency while achieving the samedetection accuracy as the state-of-the-art methods.

A novel efficient Multi-view traffic-related object detection framework