Abstract
Events cameras are ideal sensors for enabling robots to detect and track objects in highly dynamic environments due to their low latency output, high temporal resolution, and high dynamic range. In this paper, we present the Asynchronous Event Multi-Object Tracking (AEMOT) algorithm for detecting and tracking multiple objects by processing individual raw events asynchronously. AEMOT detects salient event blob features by identifying regions of consistent optical flow using a novel Field of Active Flow Directions built from the Surface of Active Events. Detected features are tracked as candidate objects using the recently proposed Asynchronous Event Blob (AEB) tracker in order to construct small intensity patches of each candidate object. A novel learnt validation stage promotes or discards candidate objects based on classification of their intensity patches, with promoted objects having their position, velocity, size, and orientation estimated at their event rate. We evaluate AEMOT on a new Bee Swarm Dataset, where it tracks dozens of small bees with precision and recall performance exceeding that of alternative event-based detection and tracking algorithms by over 37%. Source code and the labelled event Bee Swarm Dataset will be open sourced
Abstract (translated)
事件相机由于其低延迟输出、高时间分辨率和宽动态范围，是使机器人在高度动态环境中检测和跟踪物体的理想传感器。本文介绍了异步事件多目标跟踪（AEMOT）算法，该算法通过异步处理单个原始事件来实现对多个对象的检测与追踪。 AEMOT通过识别一致的光学流区域，利用新颖的活跃流方向场从活动事件表面构建出来，以检测显著的事件团块特征。然后使用最近提出的异步事件团块（AEB）跟踪器将这些被检测到的特征作为候选物体进行追踪，并为每个候选对象构造小强度补丁。一种创新的学习验证阶段根据对它们强度补丁的分类来推广或排除候选物体，被推广的对象将在其事件速率下估计位置、速度、大小和方向。 我们使用新的蜜蜂群集数据集评估了AEMOT，该数据集中它能够精确地追踪数十只小蜜蜂，并且在精度和召回率性能上超越其他基于事件的检测与跟踪算法超过37%。源代码以及标记的事件蜜蜂群集数据集将开放源码提供。 这段翻译总结了论文的关键贡献和技术细节，强调了AEMOT算法如何利用新颖的技术来提升机器人在复杂动态环境下的感知能力，并且通过实际测试展示了该方法的有效性和优越性。
URL
https://arxiv.org/abs/2505.08126
