融合两阶段匹配的多行人跟踪方法

IF 4.6 Q1 OPTICS Journal of Physics-Photonics Pub Date : 2023-11-01 DOI:10.1088/1742-6596/2632/1/012025
Xin Deng, Lijun Zhao, Ruifeng Li
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引用次数: 0

摘要

多行人跟踪是计算机视觉领域的研究热点之一。对于智能移动机器人来说,第一人称视角下的多行人跟踪可以为穿行人群提供信息,确保安全。现有的方法大多不能很好地处理遮挡和轨迹重叠问题。提出了一种融合两阶段匹配的多行人跟踪方法。首先,利用基于CenterNet的多任务学习网络对行人进行检测并获得相应的特征值;然后利用贪婪策略将检测到的行人与特征值进行匹配。在处理由于遮挡或轨迹重叠导致的行人再现时,建立样本库,实时更新样本。计算每个样本的颜色直方图和HOG特征。当行人消失时,记录行人消失的方向和最后位置,以便选择轨迹。最后,采用KM算法进行跨帧匹配。在MOT数据集上,将我们的方法与最近的一些方法进行了比较。结果表明,该方法在主要评价指标MOTA上有显著提高。
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Multi-pedestrian Tracking Method Fusing Two-stage Matching
Abstract Multi-pedestrian tracking is one of the hot topics in computer vision. For an intelligent mobile robot, multi-pedestrian tracking from a first-person perspective can provide information for navigating through a crowd and ensure safety. Most of the existing methods cannot deal with occlusion and trajectory overlap well. In this paper, a multi-pedestrian tracking method fusing two-stage matching is proposed. Firstly, the detection and the corresponding feature values of the pedestrians are obtained by a multi-task learning network based on CenterNet. Then the detected pedestrians are matched with feature values by greedy strategy. When dealing with the reappearance of pedestrians caused by occlusion or trajectory overlap, the sample database is established to update the samples in real time. The color histogram and HOG feature are calculated for each sample. When the pedestrian disappears, the direction of disappearance and the last position is recorded for the selection of trajectory. Finally, the KM algorithm is used for cross-frame matching. Our method is compared with some recent methods on MOT data sets. The result shows that our method has a significant improvement in the main evaluation index MOTA.
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来源期刊
CiteScore
10.70
自引率
0.00%
发文量
27
审稿时长
12 weeks
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