Pose-Guided Part-Based Adaptive Pyramid Features for Occluded Person Reidentification

Xiaobing Lin,Jilin Li,Zengxi Huang,Xiaoqin Tang,Jianbing Ma

doi:10.1155/2020/6694670

Abstract

Reidentifying an occluded person across nonoverlapping cameras is still a challenging task. In this work, we propose a novel pose-guided part-based adaptive pyramid neural network for occluded person reidentification. Firstly, to alleviate the impact of occlusion, we utilize pose landmarks to generate pose-guided attention maps. The attention maps will help the model focus on the nonoccluded regions. Secondly, we use pyramid pooling to extract multiscale features in order to address the scale variation problem. The generated pyramid features are then multiplied by attention maps to achieve pose-guided adaptive pyramid features. Thirdly, we propose a pose-guided body part partition scheme to deal with the alignment problem. Accordingly, the adaptive pyramid features are divided into partitions and fed into individual fully connected layers. In the end, all the part-based matching scores are fused with a weighted sum rule for person reidentification. The effectiveness of our method is clearly validated by the experimental results on two popular occluded and holistic datasets, i.e., Occluded-DukeMTMC and the Market-1501.

Highlights

Person reidentification (Re-ID) aims to retrieve a probe person/pedestrian from nonoverlapping cameras [1,2,3]
We propose a pose-guided dynamic body part partition scheme to deal with the alignment problem, in which the human parts are defined dynamically based on pose landmarks
E main contributions of our work are summarized as follows: (1) we propose a pose-guided part-based adaptive pyramid neural network (PPAPN) that uses the pose-guided attention map and pyramid pooling so as to deal with the occlusion and scale problems; (2) we propose a pose-guided dynamic body part partition scheme to tackle the alignment problem; and (3) we compare the proposed method with many well-known methods on two popular occluded and holistic Re-ID datasets; the experimental results validate that our proposed method is effective

Summary

Introduction

Person reidentification (Re-ID) aims to retrieve a probe person/pedestrian from nonoverlapping cameras [1,2,3]. It has become increasingly popular in the community due to its application potentials in video surveillance, smart retailing, activity analysis, and so on. Part-based models perform part-to-part matching and are much more robust to occlusions than the holistic models [7, 8]. They partition a person into a fixed number of parts and heavily depend on precise person detection

Methods

Results

Conclusion