论文标题
基于深视觉算法的群体行为跟踪
Swarm behavior tracking based on a deep vision algorithm
论文作者
论文摘要
社交昆虫（例如蚂蚁）的智能群体行为在不同的环境中浮出水面，有望为研究的智力研究提供见解。研究群行为要求研究人员可以随着时间的推移准确跟踪每个人。显然，在视频中手动标记单个昆虫是劳动密集型的。但是，自动跟踪方法也提出了严重的挑战：（1）个体小且外观相似； （2）频繁相互作用会导致严重和长期遮挡。随着人工智能和计算视觉技术的进步，我们希望提供一种工具来自动监测多种昆虫以应对上述挑战。在本文中，我们建议在视频中通过以下方式进行多端跟踪的检测和跟踪框架：（1）使用resnet-50作为骨架，采用两阶段对象检测框架，并编码感兴趣区域的位置，以准确地定位蚂蚁； （2）使用Resnet模型开发蚂蚁的外观描述； （3）构建长期外观序列并将其与运动信息结合在一起以实现在线跟踪。为了验证我们的方法，我们构建了一个蚂蚁数据库，其中包括来自不同室内和室外场景的10个蚂蚁视频。我们在室内视频中实现了95.7 \％mmota和81.1 \％MMOTP的最先进性能，81.8 \％mmota和81.9 \％MMOTP在户外视频中。此外，我们的方法的运行速度比现有的昆虫跟踪方法快6-10倍。实验结果表明，我们的方法为加速社会昆虫行为群体的机制提供了一种强大的工具。
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 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 construct an ant database including 10 videos of ants from different 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. Additionally, Our method runs 6-10 times faster than existing methods for insect tracking. Experimental results demonstrate that our method provides a powerful tool for accelerating the unraveling of the mechanisms underlying the swarm behavior of social insects.
