Person Re-Identification across Data Distributions Based on General Purpose DNN Object Detector

Abstract

Solving the person re-identification problem involves making associations between the same person’s appearances across disjoint camera views. Further, those associations have to be made on multiple surveillance cameras in order to obtain a more efficient and powerful re-identification system. The re-identification problem becomes particularly challenging in very crowded areas. This mainly happens for two reasons. First, the visibility is reduced and occlusions of people can occur. Further, due to congestion, as the number of possible matches increases, the re-identification is becoming challenging to achieve. Additional challenges consist of variations of lightning, poses, or viewpoints, and the existence of noise and blurring effects. In this paper, we aim to generalize person re-identification by implementing a first attempt of a general system, which is robust in terms of distribution variations. Our method is based on the YOLO (You Only Look Once) model, which represents a general object detection system. The novelty of the proposed re-identification method consists of using a simple detection model, with minimal additional costs, but with results that are comparable with those of the other existing dedicated methods.

Keywords

• video processing
• person re-identification
• triplet loss
• DNN—Deep Neural Network
• CNN—Convolutional Neural Network
• YOLO—You Only Look Once