Towards automated visual surveillance using gait for identity recognition and tracking across multiple non-intersecting cameras

Bouchrika, Imed; Carter, John N.; Nixon, Mark S.

doi:10.1007/s11042-014-2364-9

Cited by 75 publications

(24 citation statements)

References 33 publications

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“…Other methods generate view invariant features using a canonical view model. For instance, hip, knee and ankle positions [12], [13] or head and feet positions [14], are obtained from a random view and converted to the canonical view using a rectification step, before attempting recognition. View rectification is performed over entire silhouettes using a perspective projection model and a calibrated camera in [15].…”

Section: State-of-the-artmentioning

confidence: 99%

Gait recognition in the wild using shadow silhouettes

Verlekar

Soares

Correia

2018

Image and Vision Computing

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Gait recognition systems allow identification of users relying on features acquired from their body movement while walking. This paper discusses the main factors affecting the gait features that can be acquired from a 2D video sequence, proposing a taxonomy to classify them across four dimensions. It also explores the possibility of obtaining users' gait features from the shadow silhouettes by proposing a novel gait recognition system. The system includes novel methods for: (i) shadow segmentation, (ii) walking direction identification, and (iii) shadow silhouette rectification. The shadow segmentation is performed by fitting a line through the feet positions of the user obtained from the gait texture image (GTI). The direction of the fitted line is then used to identify the walking direction of the user. Finally, the shadow silhouettes thus obtained are rectified to compensate for the distortions and deformations resulting from the acquisition setup, using the proposed four-point correspondence method. The paper additionally presents a new database, consisting of 21 users moving along two walking directions, to test the proposed gait recognition system. Results show that the performance of the proposed system is equivalent to that of the state-of-the-art in a constrained setting, but performing equivalently well in the wild, where most state-of-the-art methods fail. The results also highlight the advantages of using rectified shadow silhouettes over body silhouettes under certain conditions.

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Section: State-of-the-artmentioning

confidence: 99%

Gait recognition in the wild using shadow silhouettes

Verlekar

Soares

Correia

2018

Image and Vision Computing

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“…M ost o f t he s tatic t raits s uch as fingerprint and iris have been used in r eality. But these traits are limited by distance and the interaction with subjects [Bouchrika, Carter and Nixon (2016)]. Comparing with these biometric features, gait is an important coarse feature about motion so that gait recognition is robust to low resolution.…”

Section: Introductionmentioning

confidence: 99%

Attention-Aware Network With Latent Semantic Analysis for Clothing Invariant Gait Recognition

Ling¹,

Wu²,

Li³

et al. 2019

Computers, Materials &Amp; Continua

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Gait recognition is a complicated task due to the existence of co-factors like carrying conditions, clothing, viewpoints, and surfaces which change the appearance of gait more or less. Among those co-factors, clothing analysis is the most challenging one in the area. Conventional methods which are proposed for clothing invariant gait recognition show the body parts and the underlying relationships from them are important for gait recognition. Fortunately, attention mechanism shows dramatic performance for highlighting discriminative regions. Meanwhile, latent semantic analysis is known for the ability of capturing latent semantic variables to represent the underlying attributes and capturing the relationships from the raw input. Thus, we propose a new CNN-based method which leverages advantage of the latent semantic analysis and attention mechanism. Based on discriminative features extracted using attention and the latent semantic analysis module respectively, multi-modal fusion method is proposed to fuse those features for its high fault tolerance in the decision level. Experiments on the most challenging clothing variation dataset: OU-ISIR TEADMILL dataset B show that our method outperforms other state-of-art gait approaches.

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“…In the sports domain, gait patterns have been used to assist athletes so they can perform better and safer [26,13]). In smart surveillance systems, gait signatures are used as a new type of biometric authentication [6,7]). And also, in human-computer interaction, where gait has been used to interact in video-game environments [3] or to animate virtual characters [14].…”

Section: Introductionmentioning

confidence: 99%

Benchmark RGB-D Gait Datasets: A Systematic Review

2019

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Human motion analysis has proven to be a great source of information for a wide range of applications. Several approaches for a detailed and accurate motion analysis have been proposed in the literature, as well as an almost proportional number of dedicated datasets. The relatively recent arrival of depth sensors contributed to an increasing interest in this research area and also to the emergence of a new type of motion datasets. This work focuses on a systematic review of publicly available depth-based datasets, encompassing human gait data which is used for person recognition and/or classification purposes. We have conducted this systematic review using the Scopus database. The herein presented survey, which to the best of our knowledge is the first one dedicated to this type of datasets, is intended to inform and aid researchers on the selection of the most suitable datasets to develop, test and compare their algorithms.

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Towards automated visual surveillance using gait for identity recognition and tracking across multiple non-intersecting cameras

Cited by 75 publications

References 33 publications

Gait recognition in the wild using shadow silhouettes

Gait recognition in the wild using shadow silhouettes

Attention-Aware Network With Latent Semantic Analysis for Clothing Invariant Gait Recognition

Benchmark RGB-D Gait Datasets: A Systematic Review

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