Detecting Hunts in Wildlife Videos

Niels C. Haering Richard J. Qian M. Ibrahim Sezan University of Central Florida Sharp Labs of America Orlando, FL 32816 Camas, WA 98607 Abstract We propose a three-level algorithm to detect animal hunt events in wildlife documentaries. The rst level extracts texture, color and motion features, and detects motion blobs. The mid-level employs a neural network to verify the relevance of the detected motion blobs using the extracted color and texture features. This level also generates shot summaries in terms of intermediate-level descriptors which combine low-level features from the rst level and contain results of midlevel, domain speci c inferences made on the basis of shot features. The shot summaries are then used by a domain-speci c inference process at the third level to detect the video segments that contain hunts.