555,984 research outputs found
Review of Person Re-identification Techniques
Person re-identification across different surveillance cameras with disjoint
fields of view has become one of the most interesting and challenging subjects
in the area of intelligent video surveillance. Although several methods have
been developed and proposed, certain limitations and unresolved issues remain.
In all of the existing re-identification approaches, feature vectors are
extracted from segmented still images or video frames. Different similarity or
dissimilarity measures have been applied to these vectors. Some methods have
used simple constant metrics, whereas others have utilised models to obtain
optimised metrics. Some have created models based on local colour or texture
information, and others have built models based on the gait of people. In
general, the main objective of all these approaches is to achieve a
higher-accuracy rate and lowercomputational costs. This study summarises
several developments in recent literature and discusses the various available
methods used in person re-identification. Specifically, their advantages and
disadvantages are mentioned and compared.Comment: Published 201
Recommended from our members
A Review of Techniques on Gait-Based Person Re-Identification
Copyright (c) 2023 Babak Rahi, Maozhen Li and Man Qi. Person re-identification at a distance across multiple non-overlapping cameras has been an active research area for years. In the past ten years, short-term Person re-identification techniques have made great strides in accuracy using only appearance features in limited environments. However, massive intra-class variations and inter-class confusion limit their ability to be used in practical applications. Moreover, appearance consistency can only be assumed in a short time span from one camera to the other. Since the holistic appearance will change drastically over days and weeks, the technique, as mentioned above, will be ineffective. Practical applications usually require a long-term solution in which the subject's appearance and clothing might have changed after the elapse of a significant period. Facing these problems, soft biometric features such as Gait has stirred much interest in the past years. Nevertheless, even Gait can vary with illness, ageing and emotional states, walking surfaces, shoe types, clothes types, carried objects (by the subject) and even environment clutters. Therefore, Gait is considered as a temporal cue that could provide biometric motion information. On the other hand, the shape of the human body could be viewed as a spatial signal which can produce valuable information. So extracting discriminative features from both spatial and temporal domains would benefit this research. This article examines the main approaches used in gait analysis for re-identification over the past decade. We identify several relevant dimensions of the problem and provide a taxonomic analysis of current research. We conclude by reviewing the performance levels achievable with current technology and providing a perspective on the most challenging and promising research directions.This research received no external funding
Person re-identification with soft biometrics through deep learning
Re-identification of persons is usually based on primary biometric features such as their faces, fingerprints, iris or gait. However, in most existing video surveillance systems, it is difficult to obtain these features due to the low resolution of surveillance footages and unconstrained real-world environments. As a result, most of the existing person re-identification techniques only focus on overall visual appearance. Recently, the use of soft biometrics has been proposed to improve the performance of person re-identification. Soft biometrics such as height, gender, age are physical or behavioural features, which can be described by humans. These features can be obtained from low-resolution videos at a distance ideal for person re-identification application. In addition, soft biometrics are traits for describing an individual with human-understandable labels. It allows human verbal descriptions to be used in the person re-identification or person retrieval systems. In some deep learning based person re-identification methods, soft biometrics attributes are integrated into the network to boot the robustness of the feature representation. Biometrics can also be utilised as a domain adaptation bridge for addressing the cross-dataset person re-identification problem. This chapter will review the state-of-the-art deep learning methods involving soft biometrics from three perspectives: supervised, semi-supervised and unsupervised approaches. In the end, we discuss the existing issues that are not addressed by current works
Query-guided End-to-End Person Search
Person search has recently gained attention as the novel task of finding a
person, provided as a cropped sample, from a gallery of non-cropped images,
whereby several other people are also visible. We believe that i. person
detection and re-identification should be pursued in a joint optimization
framework and that ii. the person search should leverage the query image
extensively (e.g. emphasizing unique query patterns). However, so far, no prior
art realizes this. We introduce a novel query-guided end-to-end person search
network (QEEPS) to address both aspects. We leverage a most recent joint
detector and re-identification work, OIM [37]. We extend this with i. a
query-guided Siamese squeeze-and-excitation network (QSSE-Net) that uses global
context from both the query and gallery images, ii. a query-guided region
proposal network (QRPN) to produce query-relevant proposals, and iii. a
query-guided similarity subnetwork (QSimNet), to learn a query-guided
reidentification score. QEEPS is the first end-to-end query-guided detection
and re-id network. On both the most recent CUHK-SYSU [37] and PRW [46]
datasets, we outperform the previous state-of-the-art by a large margin.Comment: Accepted as poster in CVPR 201
- …