Title
Detecting Hunts In Wildlife Videos
Abstract
We propose a three-level algorithm to detect animal hunt events in wildlife documentaries. The first 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 first level and contain results of mid-level, domain specific inferences made on the basis of shot features. The shot summaries are then used by a domain-specific inference process at the third level to detect the video segments that contain hunts.
Publication Date
1-1-1999
Publication Title
International Conference on Multimedia Computing and Systems -Proceedings
Volume
1
Number of Pages
905-999
Document Type
Article
Personal Identifier
scopus
Copyright Status
Unknown
Socpus ID
0032646439 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/0032646439
STARS Citation
Haering, Niels C.; Qian, Richard J.; and Sezan, M. Ibrahim, "Detecting Hunts In Wildlife Videos" (1999). Scopus Export 1990s. 4012.
https://stars.library.ucf.edu/scopus1990/4012