Multi-Perspective Tracking for Intelligent Vehicle

被引：16

作者：

Ji, Xiangyang ^{[1
]}

Zhang, Guanwen ^{[2
]}

Chen, Xiaogang ^{[1
]}

Guo, Qi ^{[3
]}

机构：

[1] Tsinghua Univ, Dept Automat, Beijing 100084, Peoples R China

[2] Northwestern Polytech Univ, Sch Elect & Informat, Xian 710072, Shaanxi, Peoples R China

[3] Harvard Univ, Sch Engn & Appl Sci, Cambridge, MA 02138 USA

来源：

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS | 2018年 / 19卷 / 02期

基金：

国家自然科学基金国际合作与交流项目; 中国国家自然科学基金;

关键词：

Multi-perspective tracking; multi-object tracking; intelligent vehicle; re-identification; PEOPLE REIDENTIFICATION;

D O I：

10.1109/TITS.2017.2784486

中图分类号：

TU [建筑科学];

学科分类号：

0813 ;

摘要：

The multi-camera array has drawn attention of researchers in recent years, and has been configured and deployed on intelligent vehicle to capture the panoramic views. Understanding surroundings is crucial for the ego-vehicle. This paper presents a Multi-perspective Tracking (MPT) framework for intelligent vehicle. An iterative search procedure is proposed to associate detections and tracklets in different perspectives. This procedure iteratively assigns determined states and estimates non-determined states for the detections and tracklets. An inherent determined and non-determined graph is utilized to reinforce this procedure. For more reliable associations between perspectives, a Siamese convolutional neural network is employed to learn feature representation. The supervised classification and verification signals are added to train the network. The features in different conventional stages are integrated together as the discriminative appearance model. The experiments are conducted on a MPT data set with five perspectives. The proposed framework is tested in each pair of adjacent perspectives for the ability to associate target objects between perspectives.

引用

页码：518 / 529

页数：12

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