Depth Perspective-aware Multiple Object Tracking
- URL: http://arxiv.org/abs/2207.04551v1
- Date: Sun, 10 Jul 2022 22:12:00 GMT
- Title: Depth Perspective-aware Multiple Object Tracking
- Authors: Kha Gia Quach, Huu Le, Pha Nguyen, Chi Nhan Duong, Tien Dai Bui, Khoa
Luu
- Abstract summary: DP-MOT is a real-time Depth Perspective-aware Multiple Object Tracking approach.
The proposed approach consistently achieves state-of-the-art performance compared to recent MOT methods.
- Score: 24.06104433665443
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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