Pedestrian recognition with false positive detection by model-based tracking
SPPR'07 Proceedings of the Fourth conference on IASTED International Conference: Signal Processing, Pattern Recognition, and Applications
Pedestrian recognition with false positive detection by model-based tracking
SPPRA '07 Proceedings of the Fourth IASTED International Conference on Signal Processing, Pattern Recognition, and Applications
Content-based retrieval for human motion data
Journal of Visual Communication and Image Representation
Use of nested K-means for robust head location in visual surveillance system
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Gait recognition using Hough transform and principal component analysis
ICIC'09 Proceedings of the 5th international conference on Emerging intelligent computing technology and applications
Visual affect recognition
A robust fusion method for vehicle detection in road traffic surveillance
ICIC'10 Proceedings of the Advanced intelligent computing theories and applications, and 6th international conference on Intelligent computing
Real time face detection system based edge restoration and nested k-means at frontal view
FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part II
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This paper describes an approach to the extraction of articulated objects which will be used for gait analysis. In most medical applications markers are used to determine trajectories of different body parts. This approach works without any markers. Monotony operators which compute the displacement vector field are used to initialize a contour based tracking algorithm called active rays-for several body parts which are important for gait analysis. The contours of different parts of the human body are extracted and tracked. These parts are approached by simple 3D geometric objects (blocks), which 3D position and motion are estimated for the each image of the image sequence. Then, the trajectories of the moving parts represented by the 3D blocks can be determined and used for classification of different gait disorders.