Recognizing human action from image sequences is an active area of research in computer vision. In this paper, we present a novel method for human action recognition from image se...
This paper discusses a new technique for detecting affective events using Hidden Markov Models(HMM). To map low level features of video data to high level emotional events, we per...
Abstract. Hidden Markov models are traditionally decoded by the Viterbi algorithm which finds the highest probability state path in the model. In recent years, several limitations ...
Segmentation and tracking of multiple humans in crowded situations is made difficult by interobject occlusion. We propose a model-based approach to interpret the image observations...
In this paper, we introduce a first-order probabilistic model that combines multiple cues to classify human activities from video data accurately and robustly. Our system works in...