Fast communication: Active energy image plus 2DLPP for gait recognition
Signal Processing
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
Marker-based human motion capture in multiview sequences
EURASIP Journal on Advances in Signal Processing
Using a live-in laboratory for ubiquitous computing research
PERVASIVE'06 Proceedings of the 4th international conference on Pervasive Computing
Projective kalman filter: multiocular tracking of 3D locations towards scene understanding
MLMI'05 Proceedings of the Second international conference on Machine Learning for Multimodal Interaction
Embedded assessment: overcoming barriers to early detection with pervasive computing
PERVASIVE'05 Proceedings of the Third international conference on Pervasive Computing
Gait recognition based on partitioned weighting gait energy image
IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Gait recognition based on shape and motion analysis of silhouette contours
Computer Vision and Image Understanding
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This research presents a new model-based approach toward the three-dimensional (3-D) tracking and extraction of gait and human motion. We suggest the use of a hierarchical, structural model of the human body that introduces the concept of soft kinematic constraints. These constraints take the form of a priori, stochastic distributions learned from previous configurations of the body exhibited during specific activities; they are used to supplement an existing motion model limited by hard kinematic constraints. We use time-varying parameters of the structural model to measure gait velocity, stance width, stride length, stance times, and other gait variables with multiple degrees of accuracy and robustness. To characterize tracking performance, we also introduce a novel geometric model of expected tracking failures. We demonstrate and quantify the performance of the suggested models using multi-view, video sequences of human movement captured in a complex home environment.