Abstract
Video-based vehicle detection and tracking is one of the most important components for Intelligent Transportation Systems (ITS). When it comes to road junctions, the problem becomes even more difficult due to the occlusions and complex interactions among vehicles. In order to get a precise detection and tracking result, in this work we propose a novel tracking-by-detection framework. In the detection stage, we present a sequential detection model to deal with serious occlusions. In the tracking stage, we model group behavior to treat complex interactions with overlaps and ambiguities. The main contributions of this paper are twofold: 1) Shape prior is exploited in the sequential detection model to tackle occlusions in crowded scene. 2) Traffic force is defined in the traffic scene to model group behavior, and it can assist to handle complex interactions among vehicles. We evaluate the proposed approach on real surveillance videos at road junctions and the performance has demonstrated the effectiveness of our method.
Abstract (translated)
基于视频的车辆检测与跟踪是智能交通系统（ITS）的重要组成部分之一。当涉及到道路交叉口时，由于车辆之间的封闭和复杂的相互作用，问题变得更加困难。为了得到精确的检测跟踪结果，本文提出了一种新的检测跟踪框架。在检测阶段，我们提出了一个顺序检测模型来处理严重的闭塞。在跟踪阶段，我们对群体行为进行建模，以处理具有重叠和模糊的复杂交互。本文的主要贡献有两方面：1）在序贯检测模型中利用形状先验来解决拥挤场景中的闭塞问题。2）交通力是在交通场景中定义的，用来模拟群体行为，有助于处理车辆之间复杂的相互作用。我们评估了所提出的道路交叉口实时监控视频的方法，并证明了该方法的有效性。
URL
https://arxiv.org/abs/1904.12641
