  This paper aims to tackle Multiple Object Tracking (MOT), an important
problem in computer vision but remains challenging due to many practical
issues, especially occlusions. Indeed, we propose a new real-time Depth
Perspective-aware Multiple Object Tracking (DP-MOT) approach to tackle the
occlusion problem in MOT. A simple yet efficient Subject-Ordered Depth
Estimation (SODE) is first proposed to automatically order the depth positions
of detected subjects in a 2D scene in an unsupervised manner. Using the output
from SODE, a new Active pseudo-3D Kalman filter, a simple but effective
extension of Kalman filter with dynamic control variables, is then proposed to
dynamically update the movement of objects. In addition, a new high-order
association approach is presented in the data association step to incorporate
first-order and second-order relationships between the detected objects. The
proposed approach consistently achieves state-of-the-art performance compared
to recent MOT methods on standard MOT benchmarks.
