论文标题
密集的场景多个对象跟踪，盒平面匹配
Dense Scene Multiple Object Tracking with Box-Plane Matching
论文作者
论文摘要
多对象跟踪（MOT）是计算机视觉中的重要任务。由于阻塞问题，MOT仍然具有挑战性，尤其是在密集的场景中。遵循逐个检测框架后，我们提出了盒子平面匹配（BPM）方法，以改善密集场景中的MOT Performacne。首先，我们设计了层的聚合判别模型（LADM）以滤掉嘈杂的检测。然后，为了正确关联剩余的检测，我们引入了全局注意力特征模型（GAFM）来提取外观特征，并使用它来计算历史记录踪迹和当前检测之间的外观相似性。最后，我们提出了框平面匹配策略，以根据运动和检测之间的运动相似性和外观相似性实现数据关联。凭借这三个模块的有效性，我们的团队在ACM MM Grand Challenge Hieve 2020中获得了Track-1排行榜的第一名。
Multiple Object Tracking (MOT) is an important task in computer vision. MOT is still challenging due to the occlusion problem, especially in dense scenes. Following the tracking-by-detection framework, we propose the Box-Plane Matching (BPM) method to improve the MOT performacne in dense scenes. First, we design the Layer-wise Aggregation Discriminative Model (LADM) to filter the noisy detections. Then, to associate remaining detections correctly, we introduce the Global Attention Feature Model (GAFM) to extract appearance feature and use it to calculate the appearance similarity between history tracklets and current detections. Finally, we propose the Box-Plane Matching strategy to achieve data association according to the motion similarity and appearance similarity between tracklets and detections. With the effectiveness of the three modules, our team achieves the 1st place on the Track-1 leaderboard in the ACM MM Grand Challenge HiEve 2020.
