| Summary: | Object detection and tracking are tasks that humans can perform effortlessly in most environments. Humans can readily recognize individual trees in forests and maintain unique identifiers during occlusion. For computers, on the other hand, this is a complex problem that decades of research have been dedicated to solving. This paper presents a computer vision approach to object detection and tracking tasks in forested environments. We use a state-of-the-art neural network-based detection algorithm to fit bounding boxes around individual tree stems and a simple, efficient, and deterministic multiple object tracking algorithm to maintain unique identities for stems through video frames. We trained the neural network object detector on approximately 3000 ground-truth bounding boxes of ponderosa pine trees. We show that tree stem detection can achieve an average precision of 87% using a Jaccard overlap index of 0.5. We also demonstrate the robustness of the tracking algorithm in occlusion and enter–exit–re-enter scenarios. The presented algorithms can perform object detection and tracking at 49 frames per second on a consumer-grade graphics processing unit.
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