Detecting kangaroos in the wild: the first step towards automated animal surveillance
Teng Zhang, Arnold Wiliem, Graham Hemsony, Brian C. Lovell · 2015
Recent studies in computer vision have provided new solutions to real-world problems. In this paper, we focus on using computer vision methods to assist in the study of kangaroos in the wild. In order to investigate the feasibility, we built a kangaroo image dataset from collected data from several national parks across the State of Queensland. To achieve reasonable detection accuracy, we explored a multi-pose approach and proposed a framework based on the state-of-the-art Deformable Part Model (DPM). Experiments show that the proposed framework outperformed the state-of-the-art methods on the proposed dataset. Also, the proposed vision tools are able to help our field biologists in studying kangaroo related problems such as population tracking for activity analysis.