Discovery and Segmentation of Activities in Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognition of Visual Activities and Interactions by Stochastic Parsing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Human action recognition using star skeleton
Proceedings of the 4th ACM international workshop on Video surveillance and sensor networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Recognition of Human Activities: A Survey
IEEE Transactions on Circuits and Systems for Video Technology
Human action recognition with MPEG-7 descriptors and architectures
Proceedings of the first ACM international workshop on Analysis and retrieval of tracked events and motion in imagery streams
An overview of contest on semantic description of human activities (SDHA) 2010
ICPR'10 Proceedings of the 20th International conference on Recognizing patterns in signals, speech, images, and videos
HMM based action recognition with projection histogram features
ICPR'10 Proceedings of the 20th International conference on Recognizing patterns in signals, speech, images, and videos
A method of abnormal habits recognition in intelligent space
Engineering Applications of Artificial Intelligence
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On-line action recognition from a continuous stream of actions is still an open problem with fewer solutions proposed compared to time-segmented action recognition. The most challenging task is to classify the current action while finding its time boundaries at the same time. In this paper we propose an approach capable of performing on-line action segmentation and recognition by means of batteries of HMM taking into account all the possible time boundaries and action classes. A suitable Bayesian normalization is applied to make observation sequences of different length comparable and computational optimizations are introduce to achieve real-time performances. Results on a well known action dataset prove the efficacy of the proposed method.