View-invariant Estimation of Height and Stride for Gait Recognition
ECCV '02 Proceedings of the International ECCV 2002 Workshop Copenhagen on Biometric Authentication
The Gait Identification Challenge Problem: Data Sets and Baseline Algorithm
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Body Tracking in HumanWalk from Monocular Video Sequences
CRV '05 Proceedings of the 2nd Canadian conference on Computer and Robot Vision
Body Localization in Still Images Using Hierarchical Models and Hybrid Search
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Measure Locally, Reason Globally: Occlusion-sensitive Articulated Pose Estimation
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
A Bayesian Framework for Extracting Human Gait Using Strong Prior Knowledge
IEEE Transactions on Pattern Analysis and Machine Intelligence
Model Driven Segmentation of Articulating Humans in Laplacian Eigenspace
IEEE Transactions on Pattern Analysis and Machine Intelligence
Model-based feature extraction for gait analysis and recognition
MIRAGE'07 Proceedings of the 3rd international conference on Computer vision/computer graphics collaboration techniques
Heel strike detection based on human walking movement for surveillance analysis
Pattern Recognition Letters
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Heel strike detection is an important cue for human gait recognition and detection in visual surveillance since the heel strike position can be used to derive the gait periodicity, stride and step length. We propose a novel method for heel strike detection using a gait trajectory model, which is robust to occlusion, camera view and to low resolution which can generalize to a variety of surveillance imagery. When a person walks, the movement of the head is conspicuous and sinusoidal. The highest point of the trajectory of the head occurs when the feet cross. Our gait trajectory model is constructed from trajectory data using non-linear optimization. Then, the key frames in which the heel strike takes place are extracted. A Region Of Interest (ROI) is extracted using the silhouette image of the key frame as a filter. Finally, gradient descent is applied to detect maxima which are considered to be the time of the heel strikes. The experimental results show a detection rate of 95% on two databases. The contribution of this research is the first use of the gait trajectory in the heel strike position estimation process and we contend that the approach is a new approach for basic analysis in surveillance imagery.