  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.

Computer Vision and Pattern Recognition