The AVA Multi-View Dataset for Gait Recognition

In this paper, we introduce a new multi-view dataset for gait recognition. The dataset was recorded in an indoor scenario, using six convergent cameras setup to produce multi-view videos, where each video depicts a walking human. Each sequence contains at least 3 complete gait cycles. The dataset contains videos of 20 walking persons with a large variety of body size, who walk along straight and curved paths. The multi-view videos have been processed to produce foreground silhouettes. To validate our dataset, we have extended some appearance-based 2D gait recognition methods to work with 3D data, obtaining very encouraging results. The dataset, as well as camera calibration information, is freely available for research purpose

Entropy Volumes for Viewpoint Independent Gait Recognition

Gait as biometrics has been widely used for human identi cation. However, direction changes cause di culties for most of the gait recognition systems, due to appearance changes. This study presents an e cient multi-view gait recognition method that allows curved trajectories on completely unconstrained paths for in- door environments. Our method is based on volumet- ric reconstructions of humans, aligned along their way. A new gait descriptor, termed as Gait Entropy Vol- ume (GEnV), is also proposed. GEnV focuses on cap- turing 3D dynamical information of walking humans through the concept of entropy. Our approach does not require the sequence to be split into gait cycles. A GEnV based signature is computed on the basis of the previous 3D gait volumes. Each signature is clas- si ed by a Support Vector Machine, and a majority voting policy is used to smooth and reinforce the clas- si cations results. The proposed approach is experimen- tally validated on the \AVA Multi-View Gait Dataset (AVAMVG)" and on the \Kyushu University 4D Gait Database (KY4D)". The results show that this new ap- proach achieves promising results in the problem of gait recognition on unconstrained paths

A new approach for multi-view gait recognition on unconstrained paths

Direction changes cause di culties for most of the gait recognition systems, due to appearance changes. We propose a new approach for multi-view gait recognition, which focuses on recognizing people walking on unconstrained (curved and straight) paths. To this e ect, we present a new rotation invariant gait descriptor which is based on 3D angular analysis of the movement of the subject. Our method does not require the sequence to be split into gait cycles, and is able to provide a response before processing the whole sequence. A Support Vector Machine is used for classifying, and a sliding temporal window with majority vote policy is used to reinforce the classi cation results. The proposed approach has been experimentally validated on \AVA Multi-View Dataset" and \Kyushu University 4D Gait Database" and compared with related state-of-art work. Experimental results demonstrate the e ectiveness of this approach in the problem of gait recognition on unconstrained path

Pyramidal Fisher Motion for Multiview Gait Recognition

The goal of this paper is to identify individuals by analyzing their gait. Instead of using binary silhouettes as input data (as done in many previous works) we propose and evaluate the use of motion descriptors based on densely sampled short-term trajectories. We take advantage of state-of-the-art people detectors to define custom spatial configurations of the descriptors around the target person. Thus, obtaining a pyramidal representation of the gait motion. The local motion features (described by the Divergence-Curl-Shear descriptor) extracted on the different spatial areas of the person are combined into a single high-level gait descriptor by using the Fisher Vector encoding. The proposed approach, coined Pyramidal Fisher Motion, is experimentally validated on the recent `AVA Multiview Gait' dataset. The results show that this new approach achieves promising results in the problem of gait recognition.Comment: Submitted to International Conference on Pattern Recognition, ICPR, 201

Pyramidal Fisher Motion for Multiview Gait Recognition

Submitted to International Conference on Pattern Recognition, ICPR, 2014The goal of this paper is to identify individuals by analyzing their gait. Instead of using binary silhouettes as input data (as done in many previous works) we propose and evaluate the use of motion descriptors based on densely sampled short-term trajectories. We take advantage of state-of-the-art people detectors to define custom spatial configurations of the descriptors around the target person. Thus, obtaining a pyramidal representation of the gait motion. The local motion features (described by the Divergence-Curl-Shear descriptor) extracted on the different spatial areas of the person are combined into a single high-level gait descriptor by using the Fisher Vector encoding. The proposed approach, coined Pyramidal Fisher Motion, is experimentally validated on the recent `AVA Multiview Gait' dataset. The results show that this new approach achieves promising results in the problem of gait recognition

Fisher Motion Descriptor for Multiview Gait Recognition

The goal of this paper is to identify individuals by analyzing their gait. Instead of using binary silhouettes as input data (as done in many previous works) we propose and evaluate the use of motion descriptors based on densely sampled short-term trajectories. We take advantage of state-of-the-art people detectors to de ne custom spatial con gurations of the descriptors around the target person, obtaining a rich representation of the gait motion. The local motion features (described by the Divergence-Curl-Shear descriptor [1]) extracted on the di erent spatial areas of the person are combined into a single high-level gait descriptor by using the Fisher Vector encoding [2]. The proposed approach, coined Pyramidal Fisher Motion, is experimentally validated on `CASIA' dataset [3] (parts B and C), `TUM GAID' dataset [4], `CMU MoBo' dataset [5] and the recent `AVA Multiview Gait' dataset [6]. The results show that this new approach achieves state-of-the-art results in the problem of gait recognition, allowing to recognize walking people from diverse viewpoints on single and multiple camera setups, wearing di erent clothes, carrying bags, walking at diverse speeds and not limited to straight walking paths

A multilevel paradigm for deep convolutional neural network features selection with an application to human gait recognition

Human gait recognition (HGR) shows high importance in the area of video surveillance due to remote access and security threats. HGR is a technique commonly used for the identification of human style in daily life. However, many typical situations like change of clothes condition and variation in view angles degrade the system performance. Lately, different machine learning (ML) techniques have been introduced for video surveillance which gives promising results among which deep learning (DL) shows best performance in complex scenarios. In this article, an integrated framework is proposed for HGR using deep neural network and fuzzy entropy controlled skewness (FEcS) approach. The proposed technique works in two phases: In the first phase, deep convolutional neural network (DCNN) features are extracted by pre-trained CNN models (VGG19 and AlexNet) and their information is mixed by parallel fusion approach. In the second phase, entropy and skewness vectors are calculated from fused feature vector (FV) to select best subsets of features by suggested FEcS approach. The best subsets of picked features are finally fed to multiple classifiers and finest one is chosen on the basis of accuracy value. The experiments were carried out on four well-known datasets, namely, AVAMVG gait, CASIA A, B and C. The achieved accuracy of each dataset was 99.8, 99.7, 93.3 and 92.2%, respectively. Therefore, the obtained overall recognition results lead to conclude that the proposed system is very promising