The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
The HumanID Gait Challenge Problem: Data Sets, Performance, and Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Individual Recognition Using Gait Energy Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing Humans Based on Gait Moment Image
SNPD '07 Proceedings of the Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing - Volume 02
Fast communication: Gait recognition based on dynamic region analysis
Signal Processing
Frame difference energy image for gait recognition with incomplete silhouettes
Pattern Recognition Letters
Fast communication: Active energy image plus 2DLPP for gait recognition
Signal Processing
Gait recognition using Pose Kinematics and Pose Energy Image
Signal Processing
KinectFusion: Real-time dense surface mapping and tracking
ISMAR '11 Proceedings of the 2011 10th IEEE International Symposium on Mixed and Augmented Reality
Microsoft Kinect Sensor and Its Effect
IEEE MultiMedia
Identification of humans using gait
IEEE Transactions on Image Processing
A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications
IEEE Transactions on Image Processing
Morphological grayscale reconstruction in image analysis: applications and efficient algorithms
IEEE Transactions on Image Processing
Model-based 3D gait biometrics
IJCB '11 Proceedings of the 2011 International Joint Conference on Biometrics
Gait energy volumes and frontal gait recognition using depth images
IJCB '11 Proceedings of the 2011 International Joint Conference on Biometrics
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We explore the applicability of Kinect RGB-D streams in recognizing gait patterns of individuals. Gait energy volume (GEV) is a recently proposed feature that performs gait recognition in frontal view using only depth image frames from Kinect. Since depth frames from Kinect are inherently noisy, corresponding silhouette shapes are inaccurate, often merging with the background. We register the depth and RGB frames from Kinect to obtain smooth silhouette shape along with depth information. A partial volume reconstruction of the frontal surface of each silhouette is done and a novel feature termed as Pose Depth Volume (PDV) is derived from this volumetric model. Recognition performance of the proposed approach has been tested on a data set captured using Microsoft Kinect in an indoor environment. Experimental results clearly demonstrate the effectiveness of the approach in comparison with other existing methods.