Person Search System by Enhanced Deep Feature Fusion

doi:10.3969/j.issn.1006-2475.2019.08.005

Abstract

Abstract: The deep feature of pedestrian image lacks the description of local details, and it does not have the invariance of scale, rotation, translation and illumination changes fully, which leads to the low accuracy of person search. A pedestrian search system with enhanced depth feature fusion is proposed. The system integrates the pedestrian candidate network and the pedestrian identification network into a unified framework. Among them, the pedestrian candidate network realizes the acquisition and calibration of the pedestrian boxes, while the pedestrian recognition network integrates the traditional features with geometric invariance on the basis of acquiring the deep learning characteristics, which establishes a network model with enhanced deep feature fusion. The experimental results show that the network model with enhanced depth feature fusion detects and frames pedestrians in images on SSM dataset, and has a top rate of 80.7%, which is superior to the deep feature model.

Key words: deep feature, pedestrian search, feature fusion, pedestrian boxes, geometric invariance

CLC Number:

TP111

MEI Wen-xin1,2,LIN Zhi-xian1,2,GUO Tai-liang1,2 . Person Search System by Enhanced Deep Feature Fusion[J]. Computer and Modernization, 2019, 0(08): 23-.

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