Behavior and event detection for annotation and surveillance

Visual surveillance and activity analysis is an active research field of computer vision. As a result, there are several different algorithms produced for this purpose. To obtain more robust systems it is desirable to integrate the different algorithms. To achieve this goal, the paper presents results in automatic event detection in surveillance videos, and a distributed application framework for supporting these methods. Results in motion analysis for static and moving cameras, automatic fight detection, shadow segmentation, discovery of unusual motion patterns, indexing and retrieval will be presented. These applications perform real time, and are suitable for real life applications

Integrating musicological knowledge into a probabilistic framework for chord and key extraction

In this contribution a formerly developed probabilistic framework for the simultaneous detection of chords and keys in polyphonic audio is further extended and validated. The system behaviour is controlled by a small set of carefully defined free parameters. This has permitted us to conduct an experimental study which sheds a new light on the importance of musicological knowledge in the context of chord extraction. Some of the obtained results are at least surprising and, to our knowledge, never reported as such before

Automatic human face detection for content-based image annotation

In this paper, an automatic human face detection approach using colour analysis is applied for content-based image annotation. In the face detection, the probable face region is detected by adaptive boosting algorithm, and then combined with a colour filtering classifier to enhance the accuracy in face detection. The initial experimental benchmark shows the proposed scheme can be efficiently applied for image annotation with higher fidelity