The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision and Image Understanding - Special issue on empirical evaluation of computer vision algorithms
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Maximum Entropy Markov Models for Information Extraction and Segmentation
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Segmenting motion capture data into distinct behaviors
GI '04 Proceedings of the 2004 Graphics Interface Conference
Handsignals Recognition From Video Using 3D Motion Capture Data
WACV-MOTION '05 Proceedings of the IEEE Workshop on Motion and Video Computing (WACV/MOTION'05) - Volume 2 - Volume 02
Conditional models for contextual human motion recognition
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Segmentation and recognition of motion streams by similarity search
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
A 3-dimensional sift descriptor and its application to action recognition
Proceedings of the 15th international conference on Multimedia
Local velocity-adapted motion events for spatio-temporal recognition
Computer Vision and Image Understanding
Semantic quantization of 3D human motion capture data through spatial-temporal feature extraction
MMM'08 Proceedings of the 14th international conference on Advances in multimedia modeling
Video Human Motion Recognition Using Knowledge-Based Hybrid Method
ISM '10 Proceedings of the 2010 IEEE International Symposium on Multimedia
Local descriptors for spatio-temporal recognition
SCVMA'04 Proceedings of the First international conference on Spatial Coherence for Visual Motion Analysis
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Human motion recognition in video data has several interesting applications in fields such as gaming, senior/assisted-living environments, and surveillance. In these scenarios, we may have to consider adding new motion classes (i.e., new types of human motions to be recognized), as well as new training data (e.g., for handling different type of subjects). Hence, both the accuracy of classification and training time for the machine learning algorithms become important performance parameters in these cases. In this article, we propose a knowledge-based hybrid (KBH) method that can compute the probabilities for hidden Markov models (HMMs) associated with different human motion classes. This computation is facilitated by appropriately mixing features from two different media types (3D motion capture and 2D video). We conducted a variety of experiments comparing the proposed KBH for HMMs and the traditional Baum-Welch algorithms. With the advantage of computing the HMM parameter in a noniterative manner, the KBH method outperforms the Baum-Welch algorithm both in terms of accuracy as well as in reduced training time. Moreover, we show in additional experiments that the KBH method also outperforms the linear support vector machine (SVM).