We propose a novel real-time multiple pedestrian tracker for videos acquired from both static and moving cameras in unconstrained real-world environment. In such scenes, trackers always suffer from noisy detections and frequent occlusions. Existing methods usually use complex learning approaches and a large number of training samples to get discriminative appearance features. However, this leads to high computational cost and hardly works in occlusions (missing detections) and undistinguishable appearance. Addressing this, we design a light two-stage tracker. Firstly, a shallow net with two layers of full convolution is proposed to encode appearance. Compared with other deep architectures and sophisticated learning approaches, our shallow net is efficient and robust enough without any online updating. Secondly, we design a motion model to deal with noisy detections and missing objects caused by motion blur or occlusion. By mining the motion pattern, our tracker can reliably predict the object location under challenging scenes. Furthermore, we propose a speedup version to verify our robustness and the possibility of using in online applications. Extensive experiments are implemented on multiple object tracking benchmarks, MOT15 and MOT17. The performance is competitive over a number of state-of-the-art trackers and demonstrates that our tracker is very promising for real-time applications.

我们提出了一种新颖的实时多行人跟​​踪器，用于在不受限制的现实环境中从静态和动态摄像机获取的视频。在这样的场景中，跟踪器总是遭受嘈杂的检测和频繁的遮挡。现有方法通常使用复杂的学习方法和大量的训练样本来获得有区别的外观特征。但是，这导致高计算成本，并且几乎不能在遮挡（缺少检测）和外观无法区分的情况下工作。针对这个问题，我们设计了一个轻型的两级跟踪器。首先，提出了具有两层全卷积的浅层网络来编码外观。与其他深层架构和复杂的学习方法相比，我们的浅层网络足够高效且强大，无需任何在线更新。其次，我们设计了一个运动模型，以处理由于运动模糊或遮挡而导致的嘈杂检测和丢失物体。通过挖掘运动模式，我们的跟踪器可以可靠地预测具有挑战性的场景下的对象位置。此外，我们提出了一个加速版本，以验证我们的健壮性和在在线应用程序中使用的可能性。在多个对象跟踪基准MOT15和MOT17上进行了广泛的实验。其性能在许多最新的跟踪器上都具有竞争力，并证明我们的跟踪器在实时应用中非常有前途。我们提出了一个加速版本来验证我们的健壮性以及在在线应用程序中使用它的可能性。在多个对象跟踪基准MOT15和MOT17上进行了广泛的实验。其性能在许多最新的跟踪器上都具有竞争力，并证明我们的跟踪器在实时应用中非常有前途。我们提出了一个加速版本来验证我们的健壮性以及在在线应用程序中使用它的可能性。在多个对象跟踪基准MOT15和MOT17上进行了广泛的实验。该性能在许多先进的跟踪器上都具有竞争力，并且证明了我们的跟踪器在实时应用中非常有前途。