基于ACF和粒子滤波的在线多人检测跟踪方法

T. Kokul, A. Ramanan, U. Pinidiyaarachchi
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引用次数: 9

摘要

自动检测和跟踪视频中的多人是基于计算机视觉应用的主要研究方向之一。本文提出了一种结合预训练的一般人检测器、在线训练的特定人检测器和运动跟踪器的基于检测的跟踪方法,用于在频繁遮挡的动态背景下跟踪人。采用流行的聚合通道特征(ACF)训练检测器,并采用目标特定粒子滤波器作为运动跟踪器。为了学习目标人的正确外观,特定人检测器从之前的帧中学习阳性样本,这些样本由一般人检测器和特定人检测器检测。利用检测器和跟踪器的一致检测之间的数据关联来更新个人专用检测器和运动跟踪器。特定于人的检测器在由关联运动跟踪器定义的缩小区域中搜索目标人。检测器和跟踪器的检测结合使用,以在视频序列中定位正确的目标人。在加州理工学院行人基准数据集上进行了实验。该方法在保持实时跟踪速度的同时,对最先进的跟踪器具有更好的性能。
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Online multi-person tracking-by-detection method using ACF and particle filter
Automatically detecting and tracking multiple persons in videos is one of the main research interest in computer vision based applications. This paper presents a tracking-by-detection approach for tracking people in dynamic backgrounds with frequent occlusions by combining pre-trained generic person detector, online trained person-specific detector and a motion tracker. The popular aggregate channel features (ACF) are used to train the detectors and target specific particle filter is used as motion tracker. In order to learn right appearance of a target person, person-specific detector learns positive samples from prior frames which are detected by both generic person detector and person-specific detector. Data associations among the coincident detections of the detectors and tracker are used to update the person-specific detector and motion tracker. The person-specific detector searches the target person in a reduced region, which is defined by the associate motion tracker. A careful combination of detections of both detectors and tracker are used to locate the correct target person in the video sequence. Experiments have been carried out on Caltech pedestrian benchmark dataset. The proposed method shows better performance against state-of-the-art tracker while maintaining the tracking speed in real-time.
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