Gait Recognition By Walking and Running: A Model-Based Approach

Gait is an emerging biometric for which some techniques, mainly holistic, have been developed to recognise people by their walking patterns. However, the possibility of recognising people by the way they run remains largely unexplored. The new analytical model presented in this paper is based on the biomechanics of walking and running, and will serve as the foundation of an automatic person recognition system that is invariant to these distinct gaits. A bilateral and dynamically coupled oscillator is the key concept underlying this work. Analysis shows that this new model can be used to automatically describe walking and running subjects without parameter selection. Temporal template matching that takes into account the whole sequence of a gait cycle is applied to extract the angles of thigh and lower leg rotation. The phase-weighted magnitudes of the lower order Fourier components of these rotations form the gait signature. Classification of walking and running subjects is performed using the k-nearest-neighbour classifier. Recognition rates are similar to that achieved by other techniques with a similarly sized database. Future work will investigate feature set selection to improve the recognition rate and will determine the invariance attributes, for inter- and intra- class, of both walking and running

Statistical Analysis of Dynamic Actions

Real-world action recognition applications require the development of systems which are fast, can handle a large variety of actions without a priori knowledge of the type of actions, need a minimal number of parameters, and necessitate as short as possible learning stage. In this paper, we suggest such an approach. We regard dynamic activities as long-term temporal objects, which are characterized by spatio-temporal features at multiple temporal scales. Based on this, we design a simple statistical distance measure between video sequences which captures the similarities in their behavioral content. This measure is nonparametric and can thus handle a wide range of complex dynamic actions. Having a behavior-based distance measure between sequences, we use it for a variety of tasks, including: video indexing, temporal segmentation, and action-based video clustering. These tasks are performed without prior knowledge of the types of actions, their models, or their temporal extents

A Survey of Gait Recognition Approaches Using PCA

Human identification by gait has created a great deal of interest in computer vision community due to its advantage of inconspicuous recognition at a relatively far distance. Biometric systems are becoming increasingly important, since they provide more reliable and efficient means of identity verification. Biometric gait Analysis (i.e. recognizing people from the way they walk) is one of the recent attractive topics in biometric research. It has been receiving wide attention in the area of Biometric. In Gait biometric research there are various gait recognition approaches are available. In this paper, the gait recognition approaches such as 201C;Wavelet Descriptor with ICA201D;, and 201C;Hough transform with PCA201D; are compared and discussed

Extracting Spatio-temporal Texture Signatures for Crowd Abnormality Detection

In order to achieve automatic prediction and warning of hazardous crowd behaviors, a Spatio-Temporal Volume (STV) analysis method is proposed in this research to detect crowd abnormality recorded in CCTV streams. The method starts from building STV models using video data. STV slices – called Spatio-Temporal Textures (STT) - can then be analyzed to detect crowded regions. After calculating the Gray Level Co-occurrence Matrix (GLCM) among those regions, abnormal crowd behavior can be identified, including panic behaviors and other behavioral patterns. In this research, the proposed STT signatures have been defined and experimented on benchmarking video databases. The proposed algorithm has shown a promising accuracy and efficiency for detecting crowd-based abnormal behaviors. It has been proved that the STT signatures are suitable descriptors for detecting certain crowd events, which provide an encouraging direction for real-time surveillance and video retrieval applications

An effective video processing pipeline for crowd pattern analysis

With the purpose of automatic detection of crowd patterns including abrupt and abnormal changes, a novel approach for extracting motion “textures” from dynamic Spatio-Temporal Volume (STV) blocks formulated by live video streams has been proposed. This paper starts from introducing the common approach for STV construction and corresponding Spatio-Temporal Texture (STT) extraction techniques. Next the crowd motion information contained within the random STT slices are evaluated based on the information entropy theory to cull the static background and noises occupying most of the STV spaces. A preprocessing step using Gabor filtering for improving the STT sampling efficiency and motion fidelity has been devised and tested. The technique has been applied on benchmarking video databases for proof-of-concept and performance evaluation. Preliminary results have shown encouraging outcomes and promising potentials for its real-world crowd monitoring and control applications

Gait Recognition Using Period-Based Phase Synchronization for Low Frame-Rate Videos

Abstract—This paper proposes a method for period-based gait trajectory matching in the eigenspace using phase syn-chronization for low frame-rate videos. First, a gait period is detected by maximizing the normalized autocorrelation of the gait silhouette sequence for the temporal axis. Next, a gait silhouette sequence is expressed as a trajectory in the eigenspace and the gait phase is synchronized by time stretching and time shifting of the trajectory based on the detected period. In addition, multiple period-based matching results are integrated via statistical procedures for more robust matching in the presence of fluctuations among gait sequences. Results of experiments conducted with 185 subjects to evaluate the performance of the gait verification with various spatial and temporal resolutions, demonstrate the effectiveness of the proposed method. Keywords-gait recognition; low frame rate; phase synchro-nization; gait period; PCA I