Search

We propose an efficient method for automatic detection of suspicious behavior in video surveillance data. First of all, we cluster a set of sequences labeled as normal or suspicious. Then, we assign new observation sequences to behavior clusters. We label a sequence as suspicious if it maps to an existing model of suspicious behavior or does not map to any existing model according to the corresponding HMMs. We evaluate our proposed method on a real-world video surveillance and find that the method is very effective at detecting suspicious behavior.

Human behavior recognition is an active research field in computer vision and image processing. A novel method is proposed for human behavior recognition in video image sequences. First of all, a video sequence is represented by extracting space-time interest points. Then Human behavior is represented by activities through Motion Decomposition. The activity comprises labeled bags that are composed of unlabeled instances comprising to action. Final labeled activities are used to train a strong classifier which is used to predict the labels of unseen behavior bags. Experimental results show the effectiveness of the proposed method in comparison with other related works in the literature and can also tolerate noise and interference conditions.

Key frames play a very important role in video indexing and retrieval. In this paper, we propose a novel method to extract key frames based on information theory. We use improved Bayesian Information Criterion to determining the number of key frames, and then automatically extract key frames to represent video shot based on information bottleneck cluster method. Experimental results and the comparisons with other methods on various types of video sequences show the effectiveness of the proposed method.