Dual-stream Convolutional Neural Networks for Koala Detection and Tracking

Abstract

Conservation of koalas is an urgent task for Australia given their rapidly declining numbers in the wild. To better estimate koala populations and analyse koala activity, a camera network was deployed to capture video of koalas from zoos and the wild. This led to the creation of the world's first koala video tracking dataset. Based on this dataset, a twostream convolutional neural network model was constructed to detect and track koala activity in the video. The model has two branches, one using semantic information for object detection in the original video frames, and the other using optical flow for motion information tracking. Both branches use Yolov5, which generates the positions of objects detected in colour or near-infrared video. Finally, the features generated by the two branches are fused to determine the final position of the koala in each frame. Experimental results show that the dual-stream network can significantly improve the tracking performance when compared with the baseline model that uses only semantic information for tracking.