面向公共安全监控的多摄像机车辆重识别

doi:10.19665/j.issn1001-2400.2019.04.026

Abstract

Abstract:

The existing vehicle re-identification (Re-ID) methods mostly perform Re-ID between images marked with vehicle bounding boxes, but there are no vehicle bounding boxes in the real scene; at the same time, the complexity of environment and the similarity and diversity in appearance among vehicles can also cause a low accuracy of Re-ID. Therefore, this paper proposes a multi-camera vehicle Re-ID method combining vehicle detection and recognition for the unmarked original video in the field of public safety surveillance. First, the Binary-Single Shot MultiBox vehicle detection network is proposed to obtain the vehicle bounding boxes and generate candidate database online. Second, a multi-task Siamese vehicle recognition network is designed to improve the Re-ID accuracy. Finally, the “VeRi-1501” vehicle dataset is established, which expands the number of vehicle IDs and balances the number of images for each ID in the case of different cameras. The proposed method has achieved good results in the VeRi-1501 dataset and the actual traffic scene.

Key words: public safety, unmarked video, vehicle detection, vehicle re-identification, convolutional neural network

CLC Number:

TP391.4

WANG Yanfen,ZHU Xuran,YUN Xiao,SUN Yanjing,SHI Yunkai,WANG Sainan. Vehicle re-identification by multi-cameras for public security surveillance[J].Journal of Xidian University, 2019, 46(4): 190-196.

Figures/Tables 7

References 17

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方法	平均精度	rank-1	rank-5
Bow-CN^[1]	0.122	0.339	0.537
LOMO^[1]	0.096	0.253	0.465
GoogLeNet^[1]	0.170	0.498	0.712
FACT^[1]	0.185	0.510	0.735
PROVID^[3]	0.278	0.614	0.788
DRDL^[5]	0.546	0.490	0.735
DJDL^[4]	0.786	0.723	0.857
“MT-S”	0.806	0.834	0.976

Vehicle re-identification by multi-cameras for public security surveillance

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