Survey on Reliable Deep Learning-Based Person Re-Identification Models: Are We There Yet?
arXiv:2005.00355
Abstract
Intelligent video-surveillance (IVS) is currently an active research field in computer vision and machine learning and provides useful tools for surveillance operators and forensic video investigators. Person re-identification (PReID) is one of the most critical problems in IVS, and it consists of recognizing whether or not an individual has already been observed over a camera in a network. Solutions to PReID have myriad applications including retrieval of video-sequences showing an individual of interest or even pedestrian tracking over multiple camera views. Different techniques have been proposed to increase the performance of PReID in the literature, and more recently researchers utilized deep neural networks (DNNs) given their compelling performance on similar vision problems and fast execution at test time. Given the importance and wide range of applications of re-identification solutions, our objective herein is to discuss the work carried out in the area and come up with a survey of state-of-the-art DNN models being used for this task. We present descriptions of each model along with their evaluation on a set of benchmark datasets. Finally, we show a detailed comparison among these models, which are followed by some discussions on their limitations that can work as guidelines for future research.
24 pages, 6 figures, and 2 tables, considered over than 100 papers. arXiv admin note: substantial text overlap with arXiv:1807.05284
References in corpus (10)
- Person Re-identification: Past, Present and Future
- Virtual Worlds as Proxy for Multi-Object Tracking Analysis
- PersonNet: Person Re-identification with Deep Convolutional Neural Networks
- Unsupervised Person Re-identification by Deep Asymmetric Metric Embedding
- Deep Metric Learning for Practical Person Re-Identification
- SCAN: Self-and-Collaborative Attention Network for Video Person Re-identification
- Let Features Decide for Themselves: Feature Mask Network for Person Re-identification
- Multi-scale Deep Learning Architectures for Person Re-identification
- Constrained Deep Metric Learning for Person Re-identification
- Pyramid Person Matching Network for Person Re-identification