Swarm behavior tracking based on a deep vision algorithm
Meihong Wu, Xiaoyan Cao, Shihui Guo

TL;DR
This paper presents a deep vision-based framework for accurately and efficiently tracking individual ants in videos, aiding the study of swarm behavior and embodied intelligence.
Contribution
It introduces a novel two-stage detection and appearance-based tracking method using ResNet-50, achieving high accuracy and speed in multi-ant tracking tasks.
Findings
Achieved 95.7% mMOTA and 81.1% mMOTP in indoor videos
Achieved 81.8% mMOTA and 81.9% mMOTP in outdoor videos
Runs 6-10 times faster than existing insect tracking methods
Abstract
The intelligent swarm behavior of social insects (such as ants) springs up in different environments, promising to provide insights for the study of embodied intelligence. Researching swarm behavior requires that researchers could accurately track each individual over time. Obviously, manually labeling individual insects in a video is labor-intensive. Automatic tracking methods, however, also poses serious challenges: (1) individuals are small and similar in appearance; (2) frequent interactions with each other cause severe and long-term occlusion. With the advances of artificial intelligence and computing vision technologies, we are hopeful to provide a tool to automate monitor multiple insects to address the above challenges. In this paper, we propose a detection and tracking framework for multi-ant tracking in the videos by: (1) adopting a two-stage object detection framework using…
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Taxonomy
TopicsInsect and Arachnid Ecology and Behavior · Primate Behavior and Ecology · Animal Behavior and Reproduction
MethodsAverage Pooling · *Communicated@Fast*How Do I Communicate to Expedia? · 1x1 Convolution · Residual Connection · Global Average Pooling · Max Pooling · Batch Normalization · Residual Block · Bottleneck Residual Block · Kaiming Initialization
