This study aims to tackle Multiple Object Tracking (MOT), an essential problem in computer vision that 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 unsupervised manner. Using the output from SODE, a new active pseudo-3D Kalman filter, a simple but effective extension of the Kalman filter with dynamic control variables, is proposed to update the movement of objects dynamically. 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.