In this paper, we propose a novel semi-online approach to tracking multiple people. In contrast to conventional offline approaches that take the whole image sequence as input, our semi-online approach tracks people in a frame-by-frame manner by exploring the time, space and multi-camera relationship of detection hypotheses in the near future frames. We cast the multi-people tracking task as a re-identification problem, and explicitly account for objects’ appearance changes and longer-term associations. We model our approach using a Multi-Label Markov Random Field, and introduce a fast $$\alpha $$ α -expansion algorithm to solve it efficiently. To our best knowledge, this is the first semi-online approach achieved by re-identification. It yields very promising tracking results especially in challenging cases, such as scenarios of the crowded streets where pedestrians frequently occlude each other, scenes captured with moving cameras where objects may disappear and reappear randomly, and videos under changing illuminations wherein the appearances of objects are influenced.

中文翻译：

---

基于重识别的半在线多人跟踪

在本文中，我们提出了一种新颖的半在线方法来跟踪多人。与将整个图像序列作为输入的传统离线方法相比，我们的半在线方法通过探索近期帧中检测假设的时间、空间和多相机关系，以逐帧的方式跟踪人员。我们将多人跟踪任务视为重新识别问题，并明确说明对象的外观变化和长期关联。我们使用多标签马尔可夫随机场对我们的方法进行建模，并引入了快速的 $$\alpha $$ α 扩展算法来有效地解决它。据我们所知，这是第一个通过重新识别实现的半在线方法。它产生了非常有希望的跟踪结果，尤其是在具有挑战性的情况下，