Motion Based Animal Detection in Aerial Videos

Computer vision techniques are applied to perform automatic wildlife surveying and animal monitoring. Animal detection in aerial videos is challenging because of the complexity of wild environments. In this paper, a method for moving animal detection is proposed by taking advantage of global patterns of pixel motion. In the video dataset, where animals make obvious movement against the background, motion vectors of each pixel are estimated by applying optical flow methods. A coarse segmentation then removes most parts of the background by applying a pixel velocity threshold. Based on the segmented regions, another threshold was employed to filter out negative candidates that could belong to the background. The pros and cons of this method are discussed.