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

• URL: http://arxiv.org/abs/2302.11810v1
• Date: Thu, 23 Feb 2023 06:42:37 GMT
• Title: A novel efficient Multi-view traffic-related object detection framework
• Authors: Kun Yang, Jing Liu, Dingkang Yang, Hanqi Wang, Peng Sun, Yanni Zhang, Yan Liu, Liang Song
• Abstract summary: We propose a novel traffic-related framework named CEVAS to achieve efficient object detection using multi-view video data. Results show that our framework significantly reduces response latency while achieving the same detection accuracy as the state-of-the-art methods.
• Score: 17.50049841016045
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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