Previous online 3D Multi-Object Tracking(3DMOT) methods terminate a tracklet when it is not associated with new detections for a few frames. But if an object just goes dark, like being temporarily occluded by other objects or simply getting out of FOV, terminating a tracklet prematurely will result in an identity switch. We reveal that premature tracklet termination is the main cause of identity switches in modern 3DMOT systems. To address this, we propose Immortal Tracker, a simple tracking system that utilizes trajectory prediction to maintain tracklets for objects gone dark. We employ a simple Kalman filter for trajectory prediction and preserve the tracklet by prediction when the target is not visible. With this method, we can avoid 96% vehicle identity switches resulting from premature tracklet termination. Without any learned parameters, our method achieves a mismatch ratio at the 0.0001 level and competitive MOTA for the vehicle class on the Waymo Open Dataset test set. Our mismatch ratio is tens of times lower than any previously published method. Similar results are reported on nuScenes. We believe the proposed Immortal Tracker can offer a simple yet powerful solution for pushing the limit of 3DMOT. Our code is available at https://github.com/ImmortalTracker/ImmortalTracker.
翻译:先前的在线 3D 多目标跟踪( 3DMOT) 方法在与新探测无关时终止一个轨迹。 但是, 如果一个对象只是暗淡地, 比如被其他对象暂时隐蔽或只是离开视野, 过早终止一个轨迹将会导致身份开关。 我们发现, 过早的轨迹终止是现代 3DMOT 系统中身份开关的主要原因。 为了解决这个问题, 我们提议使用轨迹预测维持已变暗物体的跟踪器( Immortal tracker ) 。 我们使用一个简单的跟踪系统, 利用轨迹预测来维持已变暗的物体的跟踪器 。 我们使用一个简单的 Kalman 过滤器来进行轨迹预测, 在目标不可见时通过预测来保存轨迹。 使用这种方法, 我们可以避免因过早的轨迹终止而导致的 96% 的车辆身份开关。 没有学习到的参数, 我们的方法可以在 0. 001 级别上实现不匹配的比率, 在 Waymo Open DASSet 测试中, 我们的不匹配率比以前公布的方法低十倍。 。 在 nuScenes 上报告类似的结果。 我们相信, 我们的 Immtrack/ 3MDMDRDRDRD可以提供一个 3 。