A novel efficient Multi-view traffic-related object detection framework
This work addresses efficiency issues in intelligent transportation systems for applications like vehicle perception, though it appears incremental in optimizing existing methods.
The paper tackled the challenge of efficient object detection in multi-view traffic video data by proposing the CEVAS framework, which reduces response latency while maintaining state-of-the-art detection accuracy.
With the rapid development of intelligent transportation system applications, a tremendous amount of multi-view video data has emerged to enhance vehicle perception. However, performing video analytics efficiently by exploiting the spatial-temporal redundancy from video data remains challenging. Accordingly, we propose a novel traffic-related framework named CEVAS to achieve efficient object detection using multi-view video data. Briefly, a fine-grained input filtering policy is introduced to produce a reasonable region of interest from the captured images. Also, we design a sharing object manager to manage the information of objects with spatial redundancy and share their results with other vehicles. We further derive a content-aware model selection policy to select detection methods adaptively. Experimental results show that our framework significantly reduces response latency while achieving the same detection accuracy as the state-of-the-art methods.