用事件相机实现高动态场景下多目标异步跟踪，精度超现有方法37%以上。
Asynchronous Multi-Object Tracking with an Event Camera
- 异步处理原始事件，通过流动方向场检测显著事件块特征
- 基于强度块分类验证候选目标，实现超过37%的精度与召回率提升
- 适用于高速动态场景，尤其适合微小物体如蜂群的实时追踪
事件相机因其低延迟输出、高时间分辨率和高动态范围，非常适合机器人在高度动态环境中检测和跟踪物体。本文提出异步事件多目标跟踪（AEMOT）算法，通过异步处理单个原始事件来检测和跟踪多个物体。AEMOT利用从活跃事件表面构建的主动流动方向场，识别具有一致光流的区域，以检测显著事件块特征。这些特征通过近期提出的异步事件块（AEB）跟踪器作为候选物体进行追踪，并构建每个候选物体的小型强度块。一个新颖的可学习验证阶段根据强度块分类结果，决定是否保留或丢弃候选物体，被保留的目标会以事件速率估计其位置、速度、大小和方向。我们在新发布的蜂群事件数据集上评估了AEMOT，该数据集包含数十只小型蜜蜂，在精度和召回率上均超过其他事件基检测与跟踪算法37%以上。源代码和标注的事件蜂群数据集将开源。
原文摘要 · Abstract (English)
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
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