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.

翻译：随着智能交通系统应用的快速发展，大量多视角视频数据涌现以增强车辆感知能力。然而，如何通过利用视频数据中的时空冗余实现高效视频分析仍具挑战性。为此，本文提出名为CEVAS的新型交通相关框架，利用多视角视频数据实现高效目标检测。简言之，我们引入细粒度的输入过滤策略，从采集图像中生成合理的感兴趣区域；同时设计共享目标管理器，管理具有空间冗余的目标信息并将其结果共享给其他车辆。进一步提出内容感知模型选择策略，自适应选择检测方法。实验结果表明，该框架在保持与最新方法相同检测精度的同时，显著降低了响应延迟。