10 research outputs found
Gait recognition for person re-identification
Person re-identification across multiple cameras is an essential task in computer vision applications, particularly tracking the same person in different scenes. Gait recognition, which is the recognition based on the walking style, is mostly used for this purpose due to that human gait has unique characteristics that allow recognizing a person from a distance. However, human recognition via gait technique could be limited with the position of captured images or videos. Hence, this paper proposes a gait recognition approach for person re-identification. The proposed approach starts with estimating the angle of the gait first, and this is then followed with the recognition process, which is performed using convolutional neural networks. Herein, multitask convolutional neural network models and extracted gait energy images (GEIs) are used to estimate the angle and recognize the gait. GEIs are extracted by first detecting the moving objects, using background subtraction techniques. Training and testing phases are applied to the following three recognized datasets: CASIA-(B), OU-ISIR, and OU-MVLP. The proposed method is evaluated for background modeling using the Scene Background Modeling and Initialization (SBI) dataset. The proposed gait recognition method showed an accuracy of more than 98% for almost all datasets. Results of the proposed approach showed higher accuracy compared to obtained results of other methods result for CASIA-(B) and OU-MVLP and form the best results for the OU-ISIR dataset
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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
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View-invariant gait person re-identification with spatial and temporal attention
This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonPerson re-identification at a distance across multiple none overlapping cameras has
been an active research area for years. In the past ten years, Short term Person Re-Id
techniques have made great strides in terms of accuracy using only appearance features
in limited environments. However, massive intraclass 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 appearance and clothing might have changed
after a significant period has elapsed. Facing these problems, soft biometric features
such as Gait have been proposed in the past. Nevertheless, even Gait can vary with
illness, ageing and changes in the emotional state, changes in walking surfaces, shoe
type, clothes type, objects carried by the subject and even clutter in the scene. Therefore,
Gait is considered 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 be very beneficial to this research. Therefore,
this thesis focuses on finding the best and most robust method to tackle the gait human Re-identification problem and solve it for practical applications. In real-world
surveillance scenarios, the human gait cycle is primarily abnormal. These abnormalities
include but not limited to temporal and spatial characteristics changes such as
walking speed, broken gait phase and most importantly, varied camera angles. Our
work performed an extensive literature study on spatial and temporal gait feature extraction
methods with a focus on deep learning. Next, we conducted a comparative
study and proposed a spatial-temporal approach for gait feature extraction using the
fusion of multiple modalities, including optical-flow, raw silhouettes and RGB images.
This approach was tested on two of the most challenging publicly available datasets for
gait recognition TUM-GAID and CASIA-B, with excellent results presented in chapter
3.
Furthermore, a modern spatial-temporal attention mechanism was proposed and
tested on CASIA-B and OULP datasets which learns salient features independent of
the gait cycle and view variations. The spatial attention layer in the proposed method
extracts the spatial feature maps using a two-layered architecture that are fused using
late fusion. It can pay attention to the identity-related salient regions in silhouette sequences
discriminatively using the spatial feature maps. The temporal attention layer
consists of an LSTM that encodes the temporal motion for silhouette sequences. It
uses the encoded output vectors in the temporal attention architecture to focus on the
most critical timesteps in the gait cycle and discard the rest. Furthermore, we improved
the performance of our method by mapping our extracted spatial-temporal gait
features to a discriminative null space for use in our Siamese architecture for crossmatching.
We also conducted an element removal experiment on each segment of our
spatial-temporal attentional network to gain insight into each component’s contribution to the performance. Our method showed outstanding robustness against abnormal
gait cycles as well as viewpoint variations on both benchmark datasets
Human gait recognition using localized Grassmann mean representatives with partial least squares regression
Gait recognition has become popular due to the rising demand for nonintrusive biometrics. At its nascent stage of development, gait recognition faces a number of challenges. The performance of a gait recognition system is sensitive towards factors like viewing angle,
clothing, shoe type, load carriage and speed changes. In this paper, the problems of gait are formulated on the Grassmann manifold. It is not difficult to obtain multiple snapshots of a walking subjects with the wide availability of camera networks. These sets of images can be modelled as low-dimensional subspaces, which can be realized naturally as points on the Grassmann manifold. Modelling image sets as low-dimensional subspaces provides not only possible clue of one’s gait, but also the common patterns of variation in the set. We present a method called Localized Grassmann Mean Representatives with Partial Least Squares Regression (LoGPLS) to infer a low-dimensional Euclidean approximation of the manifold. The notion of local mean representatives is introduced to construct multiple tangent spaces to better approximate the topological structure of the manifold. As the properties of the tangent spaces allows the Grassmann points to be evaluated in the vector space, partial least squares is applied to allow a more accurate classification of the points in a reduced space. Experiments have been conducted on four different publicly available gait databases. Empirical evidences demonstrate the effectiveness of the proposed approach in solving the various covariates in gait recognition
Gaze-Based Human-Robot Interaction by the Brunswick Model
We present a new paradigm for human-robot interaction based on social signal processing, and in particular on the Brunswick model. Originally, the Brunswick model copes with face-to-face dyadic interaction, assuming that the interactants are communicating through a continuous exchange of non verbal social signals, in addition to the spoken messages. Social signals have to be interpreted, thanks to a proper recognition phase that considers visual and audio information. The Brunswick model allows to quantitatively evaluate the quality of the interaction using statistical tools which measure how effective is the recognition phase. In this paper we cast this theory when one of the interactants is a robot; in this case, the recognition phase performed by the robot and the human have to be revised w.r.t. the original model. The model is applied to Berrick, a recent open-source low-cost robotic head platform, where the gazing is the social signal to be considered