Body-Part Enabled Wildlife Detection and Tracking in Video Sequences

Tracking wild animals through videos presents a non-intrusive and cost-effective way of gathering scientific information key for conservation. State-of-the-art research has shown convolutional neural networks to be highly accurate, however, the application of this field on wild animal tracking has had relatively little interest. This is potentially due to the challenges of varying illumination, noisy backgrounds and camouflaged animals intrinsic to the problem. The aim of this work is to explore and apply state-of-the-art research to detect and track wild animals (specifically bears and primates, including their body parts) in video sequences in real-time. Due to obstructors such as foliage being prevalent in wild animal environments, body part tracking presents a solution to detecting animals when they are obstructed. Two deep convolutional neural networks (YOLOv4 and YOLOv4-Tiny) are trained to detect and track animals in their natural habitat. By using the knowledge that an animal is composed of body parts, the score of weakly predicted bounding is boosted from the relative distance of related body parts. For tracking, the K-Means algorithm is used to locate the average position of each animal in frame. With the introduction of a body-part confidence boosting, the detection rate can be increased by approximately 2% for a weakly predicted class.