Trajectories normalization for viewpoint invariant gait recognition

Jean, Frédéric; Bergevin, Robert; Albu, Alexandra Branzan

doi:10.1109/icpr.2008.4761312

Cited by 8 publications

(3 citation statements)

References 6 publications

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“…Kale et al [26] proposed a method to obtain the side-view gaits from any arbitrary view by using the perspective projection model. Similarly, Jean et al [27] normalized the body trajectories of any arbitrary view to a standard plane so that their similarities can be compared directly. Han et al [28] developed a statistical approach for view-insensitive gait recognition by analyzing the common properties of GEI along different directions.…”

Section: B Cross-view Gait Recognitionmentioning

confidence: 99%

GII Representation-Based Cross-View Gait Recognition by Discriminative Projection With List-Wise Constraints

Zhang

Chen

et al. 2018

IEEE Trans. Cybern.

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Remote person identification by gait is one of the most important topics in the field of computer vision and pattern recognition. However, gait recognition suffers severely from the appearance variance caused by the view change. It is very common that gait recognition has a high performance when the view is fixed but the performance will have a sharp decrease when the view variance becomes significant. Existing approaches have tried all kinds of strategies like tensor analysis or view transform models to slow down the trend of performance decrease but still have potential for further improvement. In this paper, a discriminative projection with list-wise constraints (DPLC) is proposed to deal with view variance in cross-view gait recognition, which has been further refined by introducing a rectification term to automatically capture the principal discriminative information. The DPLC with rectification (DPLCR) embeds list-wise relative similarity measurement among intraclass and inner-class individuals, which can learn a more discriminative and robust projection. Based on the original DPLCR, we have introduced the kernel trick to exploit nonlinear cross-view correlations and extended DPLCR to deal with the problem of multiview gait recognition. Moreover, a simple yet efficient gait representation, namely gait individuality image (GII), based on gait energy image is proposed, which could better capture the discriminative information for cross view gait recognition. Experiments have been conducted in the CASIA-B database and the experimental results demonstrate the outstanding performance of both the DPLCR framework and the new GII representation. It is shown that the DPLCR-based cross-view gait recognition has outperformed the-state-of-the-art approaches in almost all cases under large view variance. The combination of the GII representation and the DPLCR has further enhanced the performance to be a new benchmark for cross-view gait recognition.

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Section: B Cross-view Gait Recognitionmentioning

confidence: 99%

GII Representation-Based Cross-View Gait Recognition by Discriminative Projection With List-Wise Constraints

Zhang

Chen

et al. 2018

IEEE Trans. Cybern.

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“…Few gait analysis methods currently consider wide fields of views. This paper represents an extension of the work presented in [33]. A new method to determine foot labels (left or right) is presented.…”

Section: Introductionmentioning

confidence: 99%

Towards view-invariant gait modeling: Computing view-normalized body part trajectories

Jean

Albu

Bergevin

2009

Pattern Recognition

Self Cite

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This paper proposes an approach to compute view-normalized body part trajectories of pedestrians walking on potentially non-linear paths. The proposed approach finds applications in gait modeling, gait biometrics, and in medical gait analysis. Our approach uses the 2D trajectories of both feet and of the head extracted from the tracked silhouettes. On that basis, it computes the apparent walking (sagittal) planes for each detected gait half-cycle. A homography transformation is then computed for each walking plane to make it appear as if walking was observed from a fronto-parallel view. Finally, each homography is applied to head and feet trajectories over each corresponding gait half-cycle. View normalization makes head and feet trajectories appear as if seen from a fronto-parallel viewpoint, which is assumed to be optimal for gait modeling purposes. The proposed approach is fully automatic as it requires neither manual initialization nor camera calibration. An extensive experimental evaluation of the proposed approach confirms the validity of the normalization process.

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“…The approach cannot cope with large view angle changes under which gait sequences of different views can have little overlap. Extracting normalized trajectories of body parts is another view invariant feature based approach [9]. However tracking of body parts is unreliable due to self-occlusion.…”

Section: Introductionmentioning

confidence: 99%

Cross View Gait Recognition Using Correlation Strength

Bashir

Xiang

Gong

2010

Procedings of the British Machine Vision Conference 2010

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Among various factors that can affect the performance of gait recognition, changes in viewpoint pose the biggest problem. In this work, we develop a novel approach to cross-view gait recognition with the view angle of a probe gait sequence unknown. We formulate a Gaussian Process (GP) classification framework to estimate the view angle of each probe gait sequence. To measure the similarity of gait sequences captured at different view angles, we model the correlation of gait sequences from different views using Canonical Correlation Analysis (CCA) and use the correlation strength as similarity measure. This differs significantly from existing approaches, which reconstruct gait features in different views either through 2D view transformation or 3D calibration. Without explicit reconstruction, our approach can cope with feature mis-match across view and is more robust against feature noise. Our experiments validate that the proposed method significantly outperforms the existing state-of-the-art methods.

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Trajectories normalization for viewpoint invariant gait recognition

Cited by 8 publications

References 6 publications

GII Representation-Based Cross-View Gait Recognition by Discriminative Projection With List-Wise Constraints

GII Representation-Based Cross-View Gait Recognition by Discriminative Projection With List-Wise Constraints

Towards view-invariant gait modeling: Computing view-normalized body part trajectories

Cross View Gait Recognition Using Correlation Strength

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