Analysis of Thinning Algorithms Using Mathematical Morphology
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Faces in Images: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Medial axis for chamfer distances: computing look-up tables and neighbuorhoods in 2D or 3D
Pattern Recognition Letters
Skeleton-Based Motion Capture for Robust Reconstruction of Human Motion
CA '00 Proceedings of the Computer Animation
The Journal of Machine Learning Research
Detecting Pedestrians Using Patterns of Motion and Appearance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Fusion of Static and Dynamic Body Biometrics for Gait Recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Recognizing Human Actions: A Local SVM Approach
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Skin Segmentation Using Color Pixel Classification: Analysis and Comparison
IEEE Transactions on Pattern Analysis and Machine Intelligence
View-Invariant Human Activity Recognition Based on Shape and Motion Features
ISMSE '04 Proceedings of the IEEE Sixth International Symposium on Multimedia Software Engineering
Actions Sketch: A Novel Action Representation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Learning silhouette features for control of human motion
ACM Transactions on Graphics (TOG)
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Detection of Fence Climbing from Monocular Video
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Informative Shape Representations for Human Action Recognition
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Human action recognition using star skeleton
Proceedings of the 4th ACM international workshop on Video surveillance and sensor networks
A survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Robust recognition and segmentation of human actions using HMMs with missing observations
EURASIP Journal on Applied Signal Processing
A 3-dimensional sift descriptor and its application to action recognition
Proceedings of the 15th international conference on Multimedia
IEEE Transactions on Pattern Analysis and Machine Intelligence
Human Activity Recognition with Metric Learning
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
International Journal of Computer Vision
Hierarchical space-time model enabling efficient search for human actions
IEEE Transactions on Circuits and Systems for Video Technology
Simultaneous tracking of multiple body parts of interacting persons
Computer Vision and Image Understanding
A survey on vision-based human action recognition
Image and Vision Computing
Activity recognition using semi-Markov models on real world smart home datasets
Journal of Ambient Intelligence and Smart Environments
Real-time human pose recognition in parts from single depth images
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Human Activity Recognition Based on Silhouette Directionality
IEEE Transactions on Circuits and Systems for Video Technology
Human activity recognition using multi-features and multiple kernel learning
Pattern Recognition
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This paper presents a novel method for detecting distal limb segments for accurate skeletonization of human limbs in visual data for human action recognition. After background subtraction, a medial axis transform algorithm is applied to the body silhouette to detect the torso and the limbs. Then, a nine-segment skeleton model is fitted to the medial axis using a line fitting algorithm. The fitting is performed independently for each limb to speed-up the fitting process, avoiding the combinatorial complexity problems. The nine-segment skeleton model is used to provide precise endpoints of the distal segments of each limb which are reduced to centroids for efficient action representation. We believe that the distal limb segments such as forearms and shins provide sufficient and compact information for human action recognition. Each limb centroid is described by its angle, with respect to the vertical body axis, to create a six-element descriptor vector to represent the position of the torso and five angles for limb segments. The nine-segment skeleton model is detected and tracked without any manual initialization. A Gaussian Mixture Model is used to represent action descriptors for several human actions. Then, maximum log-likelihood criterion is utilized to classify actions. To evaluate our approach, we used three action datasets with different resolution and the results are compared with other approaches. As a result, a maximum average recognition rate of 98% is achieved for high resolution dataset and a minimum 90% for low resolution dataset.