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

Socpus ID

0032646439 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/0032646439

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