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

2.5D multi-view gait recognition based on point cloud registration

This paper presents a method for modeling a 2.5-dimensional (2.5D) human body and extracting the gait features for identifying the human subject. To achieve view-invariant gait recognition, a multi-view synthesizing method based on point cloud registration (MVSM) to generate multi-view training galleries is proposed. The concept of a density and curvature-based Color Gait Curvature Image is introduced to map 2.5D data onto a 2D space to enable data dimension reduction by discrete cosine transform and 2D principle component analysis. Gait recognition is achieved via a 2.5D view-invariant gait recognition method based on point cloud registration. Experimental results on the in-house database captured by a Microsoft Kinect camera show a significant performance gain when using MVSM

Multiple Views Effective for Gait Recognition Based on Contours

Gait is one of well recognized biometrics that has been widely used for human identification. However, the current gait recognition might have difficulties due to viewing angle being changed. This is because the viewing angle under which the gait signature database was generated may not be the same as the viewing angle when the probe data are obtained. This paper present an effective multi-view gait recognition based on motion contour (MVGRMC) approach which tackles the problems mentioned above.  Initially the background modeling is done from a video sequence. Subsequently, the moving foreground objects in the individual image frames are segmented using the background subtraction algorithm. Then, the morphological skeleton operator is used to track the moving silhouettes of a walking , Finally, when a video sequence is fed, the proposed system recognizes the gait features and thereby humans, based on self-similarity measure. The proposed system is evaluated using gait databases and the experimentation on outdoor video sequences demonstrates that the proposed algorithm achieves a pleasing recognition performance. The proposed algorithm can significantly improve the multiple view gait recognition performance when being compared to the similar methods. These results are illustrated with practical examples on popular gait databases. Keywords: Gait Recognition; Biometric; silhouette; Motion analysis; Feature extractio

Gait Analysis for Classification

This thesis describes a representation of gait appearance for the purpose of person identification and classification. This gait representation is based on simple localized image features such as moments extracted from orthogonal view video silhouettes of human walking motion. A suite of time-integration methods, spanning a range of coarseness of time aggregation and modeling of feature distributions, are applied to these image features to create a suite of gait sequence representations. Despite their simplicity, the resulting feature vectors contain enough information to perform well on human identification and gender classification tasks. We demonstrate the accuracy of recognition on gait video sequences collected over different days and times and under varying lighting environments. Each of the integration methods are investigated for their advantages and disadvantages. An improved gait representation is built based on our experiences with the initial set of gait representations. In addition, we show gender classification results using our gait appearance features, the effect of our heuristic feature selection method, and the significance of individual features

Influence of frictions on gait optimization of a biped robot with an anthropomorphic knee

This paper presents the energy consumption of a biped robot with a new modelled structure of knees which is called rolling knee (RK). The dynamic model, the actuators and the friction coefficients of the gear box are known. The optimal energy consumption can also be calculated. The first part of the paper is to validate the new kinematic knee on a biped robot by comparing the energy consumption during a walking step of the identical biped but with revolute joint knees. The cyclic gait is given by a succession of Single Support Phase (SSP) followed by an impact. The gait trajectories are parameterized by cubic spline functions. The energetic criterion is minimized through optimization while using the simplex algorithm and Lagrange penalty functions to meet the constraints of stability and deflection of the mobile foot. An analysis of the friction coefficients is done by simulation to compare the human characteristics to the robot with RK. The simulation results show an energy consumption reduction through the biped with rolling knee configuration. The influence of friction coefficients shows the energy consumption of biped robot is close to that of the human.ANR-09-SEGI-011-R2A2; French National Research Agenc