Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking and Object Classification for Automated Surveillance
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Detecting Objects, Shadows and Ghosts in Video Streams by Exploiting Color and Motion Information
ICIAP '01 Proceedings of the 11th International Conference on Image Analysis and Processing
A Computational Model for Periodic Pattern Perception Based on Frieze and Wallpaper Groups
IEEE Transactions on Pattern Analysis and Machine Intelligence
Wildlife video key-frame extraction based on novelty detection in semantic context
Multimedia Tools and Applications
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This paper presents a framework for background detection in video. First, key frames are extracted to capture background change in video and reduce the magnitude of the data. Then we analyze the content of the key frames to determine whether there is an interesting background in them. A time-constrained clustering algorithm is exploited for key frame extraction. Background detection in the key frame is done with color and texture cues. The illumination varies much in natural scenes. To deal with the varying illumination, color is modeled with three sub-models: strong light, normal light and weak light. The connectivity of background pixels is used to reduce the computing cost of texture. Experimental results show that background can be detected simply and efficiently under the framework.