Multiobject Tracking of Wildlife in Videos Using Few-Shot Learning.

Abstract

Simple SummaryVideo recordings enable scientists to estimate species’ presence, richness, abundance, demography, and activity. The increasing popularity of camera traps has led to a growing interest in developing approaches to more efficiently process images. Advanced artificial intelligence systems can automatically find and identify the species captured in the wild, but they are hampered by dependence on large samples. However, many species rarely occur, such as endangered species, and only a few shot samples are available. Building on recent advances in deep learning and few-shot learning technologies, we developed a multiobject-tracking approach based on a tracking-by-detection paradigm for wildlife to improve multiobject-tracking performance. We hope that it will be beneficial to ecology and wildlife biology by speeding up the process of multiobject tracking in the wild.Camera trapping and video recording are now ubiquitous in the study of animal ecology. These technologies hold great potential for wildlife tracking, but are limited by current learning approaches, and are hampered by dependence on large samples. Most species of wildlife are rarely captured by camera traps, and thus only a few shot samples are available for processing and subsequent identification. These drawbacks can be overcome in multiobject tracking by combining wildlife detection and tracking with few-shot learning. This work proposes a multiobject-tracking approach based on a tracking-by-detection paradigm for wildlife to improve detection and tracking performance. We used few-shot object detection to localize objects using a camera trap and direct video recordings that could augment the synthetically generated parts of separate images with spatial constraints. In addition, we introduced a trajectory reconstruction module for better association. It could alleviate a few-shot object detector’s missed and false detections; in addition, it could optimize the target identification between consecutive frames. Our approach produced a fully automated pipeline for detecting and tracking wildlife from video records. The experimental results aligned with theoretical anticipation according to various evaluation metrics, and revealed the future potential of camera traps to address wildlife detection and tracking in behavior and conservation.