We propose a three-level video event detection algorithm and apply it to animal hunt detection 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 whether the motion blobs belong to objects of interest. 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 events of interest, e.g., hunts. Event based video indexing, summarization and browsing are among the applications of the proposed approach.