Résumé: With the development of computer vision technology, many advanced computer vision methods have been successfully applied to animal detection, tracking, recognition and behavior analysis, which is of great help to ecological protection, biodiversity conservation and environmental protection. As existing datasets applied to target tracking contain various kinds of common objects, but rarely focus on wild animals, this paper proposes the first benchmark, named Wild Animal Tracking Benchmark (WATB), to encourage further progress of research and applications of visual object tracking. WATB contains more than 203,000 frames and 206 video sequences, and covers different kinds of animals from land, sea and sky. The average length of the videos is over 980 frames. Each video is manually labelled with thirteen challenge attributes including illumination variation, rotation, deformation, and so on. In the dataset, all frames are annotated with axis-aligned bounding boxes. To reveal the performance of these existing tracking algorithms and provide baseline results for future research on wild animal tracking, we benchmark a total of 38 state-of-the-art trackers and rank them according to tracking accuracy. Evaluation results demonstrate that the trackers based on deep networks perform much better than other trackers like correlation filters. Another finding on the basis of the evaluation results is that wild animals tracking is still a big challenge in computer vision community.

Langue: Anglais