View-invariant Estimation of Height and Stride for Gait Recognition
ECCV '02 Proceedings of the International ECCV 2002 Workshop Copenhagen on Biometric Authentication
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
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
Detection human motion with heel strikes for surveillance analysis
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
Localizing people in multi-view environment using height map reconstruction in real-time
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 low resolution. 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 (stance) and the lowest point is when the gait stride is the largest (heel strike). Our gait trajectory model is constructed from trajectory data using non-linear optimisation. Then, the key frames in which the heel strikes take place are calculated. A Region Of Interest (ROI) is extracted using the silhouette image of the key frame as a filter. For candidate detection, Gradient Descent is applied to detect maxima which are considered to be the time of the heel strikes. For candidate verification, two filtering methods are used to reconstruct the 3D position of a heel strike using the given camera projection matrix. The contribution of this research is the first use of the gait trajectory in the heel strike position estimation process and we contend that it is a new approach for basic analysis in surveillance imagery.