Efficient Multi-Object Tracking on Unmanned Aerial Vehicle
Abstract
Multi-object tracking has been well studied in the field of computer vision. Meanwhile, with the advancement of the Unmanned Aerial Vehicles (UAV) technology, the flexibility and accessibility of UAV draws research attention to deploy multi-object tracking on UAV. The conventional solutions usually adapt using the ”tracking-by-detection” paradigm. Such a paradigm has the structure where tracking is achieved through detecting objects in consecutive frames and then associating them with re-identification. However, the dynamic background, crowded small objects, and limited computational resources make multi-object tracking on UAV more challenging. Providing energy-efficient multi-object tracking solutions on the drone-captured video is critically demanded by the research community. To stimulate innovation in both industry and academia, we organized the 2021 LowPower Computer Vision Challenge with a UAV Video track focusing on multi-class multiobject tracking with customized UAV video. This thesis analyzes the qualified submissions of 17 different teams and provides a detailed analysis of the best solution. Methods and future directions for energy-efficient AI and computer vision research are discussed. The solutions and insights presented in this thesis are expected to facilitate future research and applications in the field of low-power vision on UAV. With the knowledge gathered from the submissions, an optical flow oriented multi-object tracking framework, named OF-MOT, is proposed to address the similar problem with a more realistic drone-captured video dataset. OF-MOT uses the motion information of each detected object of the previous frame to detect the current frame, then applies a customized object tracker using the motion information to associate the detected instances. OF-MOT is evaluated on a drone-captured video dataset and achieves 24 FPS with 17% accuracy on a modern GPU Titan X, showing that the optical flow can effectively improve the multi-object tracking. Both competition results analysis and OF-MOT provide insights or experiment results regarding deploying multi-object tracking on UAV. We hope these findings will facilitate future research and applications in the field of UAV vision.
Degree
M.Sc.
Advisors
Lu, Purdue University.
Subject Area
Aerospace engineering|Artificial intelligence|Robotics|Transportation
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