This paper presents a robust multi-class multi-object tracking (MCMOT)
formulated by a Bayesian filtering framework. Multi-object tracking for
unlimited object classes is conducted by combining detection responses and
changing point detection (CPD) algorithm. The CPD model is used to observe
abrupt or abnormal changes due to a drift and an occlusion based spatiotemporal
characteristics of track states. The ensemble of convolutional neural network
(CNN) based object detector and Lucas-Kanede Tracker (KLT) based motion
detector is employed to compute the likelihoods of foreground regions as the
detection responses of different object classes. Extensive experiments are
performed using lately introduced challenging benchmark videos; ImageNet VID
and MOT benchmark dataset. The comparison to state-of-the-art video tracking
techniques shows very encouraging results.

本文提出了一个稳健的多类别多对象跟踪（MCMOT）模型，采用贝叶斯滤波框架进行建模；通过结合检测响应和 CPD 算法，对无限对象类别进行多对象跟踪；使用 CPD 模型观察跟踪状态的时空特征，以观察漂移和遮挡等突变或异常变化；采用基于卷积神经网络的物体探测器和基于 Lucas-Kanede 跟踪器的运动探测器计算出前景区域的似然度作为不同物体类别的检测响应。通过最近推出的具有挑战性的基准视频 ImageNet VID 和 MOT 基准数据集进行了大量实验。与最先进的视频跟踪技术相比，结果非常鼓舞人心。