Accurate detection and tracking of ants in indoor and outdoor environments

Abstract

AbstractMonitoring social insects’ activity is critical for biologists researching their group mechanism. Manually labelling individual insects in a video is labour-intensive. Automated tracking social insects is particularly challenging: (1) individuals are small and similar in appearance; (2) frequent interactions with each other cause severe and long-term occlusion. We propose a detection and tracking framework for ants by: (1) adopting a two-stage object detection framework using ResNet-50 as backbone and coding the position of regions of interest to locate ants accurately; (2) using the ResNet model to develop the appearance descriptors of ants; (3) constructing long-term appearance sequences and combining them with motion information to achieve online tracking. To validate our method, we build a video database of ant colony captured in both indoor and outdoor scenes. We achieve a state-of-the-art performance of 95.7% mMOTA and 81.1% mMOTP in indoor videos, 81.8% mMOTA and 81.9% mMOTP in outdoor videos. Our method runs 6-10 times faster than existing methods for insect tracking. The datasets and code are made publicly available, we aim to contribute to an automated tracking tool for biologists in relevant domains.Author summaryThe research on the group behavior of social insects is in great favor with biologists. But before analysis, each insect needs to be tracked separately in a video. Obviously, that is a time-consuming and labor-intensive work. In this manuscript, we introduce a detection and tracking framework that can automatically track the movement of ants in a video scene. The software first uses a residual network to detect the positions of ants, then learns the appearance descriptor of each ant as appearance information via another residual network. Furthermore, we obtain motion information of each ant by using the Kalman filter. Combining with appearance and motion information, we can accurately track every ant in the ant colony. We validate the performance of our framework using 4 indoor and 5 outdoor videos, including multiple ants. We invite interested readers to apply these methods using our freely available software.