Simple Online Visual Object Tracker Fusion based on Distributed Kalman Filtering

Simple Online Visual Object Tracker Fusion based on Distributed Kalman Filtering

ZHONG, Yigen (TU Delft Mechanical, Maritime and Materials Engineering)

Kooij, J.F.P. (mentor)
Tömen, N. (mentor)
Caesar, H.C. (graduation committee)
Feng, C. (graduation committee)

Delft University of Technology

Mechanical Engineering | Vehicle Engineering | Cognitive Robotics

2023-04-18

Tracker-level fusion (TLF) is recognized as an effective approach to comprehensively improve visual object tracking performance by combining the capabilities of multiple baseline trackers. Although there is considerable interest in TLF, there are still challenges related to insufficient understanding, high cost, and unstable performance that make studying TLF difficult. In this thesis, I begin with an explicit summary of the overall pipeline of TLF which has significant guidance for TLF study. Additionally, I conduct a deep analysis of the positive and negative effects of baseline trackers to fully understand their influence on TLF. For visual object tracking, I propose three TLF frameworks based on three Distributed Kalman filters which are optimized for different scenarios and enable the fusion of different baseline trackers to enhance tracking performance. My TLF frameworks fuse the tracking results of different baseline trackers based on the principle of minimal trace to produce fusion results.Additionally, they exhibit superior and stable performance with general baseline tracker requirements, while also being simple, online, and real-time.Furthermore, the proposed frameworks can benefit from state-of-the-art baseline trackers over time, which will further improve their tracking performance. The proposed analysis and frameworks are studied extensively on three challenging benchmarks: generic tracking OTB2015, short-term tracking GOT-10k, and long-term tracking LaSOT. At over 240 FPS, the state-of-the-art success AUC score of 72.7% is achieved on OTB2015.

Visual object tracking
Tracker-level fusion
Distributed Kalman filtering

http://resolver.tudelft.nl/uuid:7826892f-7f7a-48ae-8bab-8a2764b5e6bf

Student theses

master thesis

© 2023 Yigen ZHONG