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

TL;DR

This paper introduces CEVAS, an efficient multi-view traffic object detection framework that reduces latency and maintains accuracy by filtering inputs, sharing object info, and adaptively selecting detection models.

Contribution

The paper presents a novel framework combining input filtering, object sharing, and adaptive model selection for efficient multi-view traffic object detection.

Findings

Significantly reduces response latency.

Maintains detection accuracy comparable to state-of-the-art methods.

Effectively manages spatial redundancy among multi-view data.

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.