Abstract

Multiple object tracking (MOT) at road intersections is a major research area in the field of intelligent transportation systems (ITS). In dynamic scenes like urban traffic, there is a variety of moving objects ranging from simple pedestrians to large trucks. Occlusion due to the interaction between these moving objects is one of the major problems in traffic tracking and monitoring at intersections. This paper presents an occlusion handling approach using an efficient feature-based multiple object tracking technique. A considerable amount of research work has been done in the area of occlusion handling but most of the proposed techniques can either handle occlusion of only two objects or fail to segment the individual vehicles throughout occlusion. The proposed approach uses radius nearest neighbour (RNN) classification to group the unmatched feature points along with matched feature points to update the object model. In order to validate the efficiency of this method, it is tested on four different urban traffic sequences from the Urban Tracker dataset and four traffic sequences from the Ko-PER Intersection Dataset. Experimental results depict the tracking approach of RNN based scheme compared to the other schemes for better occlusion handling and individual object segmentation.

中文翻译：

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基于半径最近邻的遮挡处理特征分类

摘要

道路交叉口的多对象跟踪（MOT）是智能交通系统（ITS）领域的主要研究领域。在诸如城市交通这样的动态场景中，有各种各样的移动物体，从简单的行人到大型卡车。由于这些运动对象之间的相互作用而造成的遮挡是交叉路口交通跟踪和监控中的主要问题之一。本文提出了一种使用基于特征的高效多对象跟踪技术的遮挡处理方法。在遮挡处理领域已经进行了大量研究工作，但是大多数提议的技术只能处理两个物体的遮挡，或者无法在整个遮挡中分割单个车辆。所提出的方法使用半径最近邻（RNN）分类将不匹配的特征点与匹配的特征点组合在一起以更新对象模型。为了验证此方法的效率，对来自Urban Tracker数据集的四个不同城市交通序列和来自Ko-PER交叉点数据集的四个交通序列进行了测试。实验结果表明，与其他方案相比，基于RNN的方案具有更好的遮挡处理和单个对象分割的跟踪方法。