A Hierarchical Scheme for Video-Based Person Re-identification Using Lightweight PCANet and Handcrafted LOMO Features  

作　　者：LI Youjiao ZHUO Li LI Jiafeng ZHANG Jing 

机构地区：[1]Beijing Key Laboratory of Computational Intelligence and Intelligent System,Beijing University of Technology,Beijing 100124,China [2]Faculty of Information Technology,College of Micro-electronics,Beijing University of Technology,Beijing 100124,China [3]College of Computer Science and Technology,Shandong University of Technology,Zibo 255000,China

出　　处：《Chinese Journal of Electronics》2021年第2期289-295,共7页电子学报（英文版）

基　　金：supported by the National Natural Science Foundation of China(No.61531006,No.61602018,and No.61701011);Beijing Municipal Natural Science Foundation Cooperation Beijing Education Committee(No.201910005007,No.KZ201810005002)。

摘　　要：A two-level hierarchical scheme for video-based person re-identification(re-id)is presented,with the aim of learning a pedestrian appearance model through more complete walking cycle extraction.Specifically,given a video with consecutive frames,the objective of the first level is to detect the key frame with lightweight Convolutional neural network(CNN)of PCANet to reflect the summary of the video content.At the second level,on the basis of the detected key frame,the pedestrian walking cycle is extracted from the long video sequence.Moreover,local features of Local maximal occurrence(LOMO)of the walking cycle are extracted to represent the pedestrian’s appearance information.In contrast to the existing walking-cycle-based person re-id approaches,the proposed scheme relaxes the limit on step number for a walking cycle,thus making it flexible and less affected by noisy frames.Experiments are conducted on two benchmark datasets:PRID 2011 and i LIDS-VID.The experimental results demonstrate that our proposed scheme outperforms the six state-of-art video-based re-id methods,and is more robust to the severe video noises and variations in pose,lighting,and camera viewpoint.

关 键 词：Video-based person re-identification Convolutional neural network Key frame detection Walking cycle extraction 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]