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Unmanned Aerial Vehicle Object Tracking by Correlation Filter with Adaptive Appearance Model
Xizhe Xue1, Ying Li2, Qiang Shen
1School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an 710129, Shaanxi, China. xuexizhe@mail.nwpu.edu.cn.
Sensors (Basel, Switzerland)
|August 24, 2018
Summary
This study introduces an efficient object tracking method for unmanned aerial vehicles (UAVs) to overcome challenges like platform motion and image instability. The approach uses fused features and cellular automata for accurate and stable aerial tracking.
Area of Science:
- Computer Vision
- Robotics
- Artificial Intelligence
Background:
- Unmanned aerial vehicles (UAVs) are increasingly used for applications like navigation and search and rescue.
- Visual tracking in UAV videos faces challenges such as aspect ratio change, viewpoint change, and scale variation due to platform motion and image instability.
Purpose of the Study:
- To propose an efficient object tracking method specifically designed for UAV videos.
- To address the challenges of real-world aerial tracking, including platform motion and image instability.
Main Methods:
- Constructing fused features to simultaneously capture gradient information and color characteristics.
- Utilizing cellular automata for accurate and sparse updating of the target's appearance template.
- Developing a high-confidence model updating strategy based on a stability function.
Main Results:
- The proposed method demonstrates efficiency in object tracking within UAV videos.
- Evaluations on the UAV123 dataset validate the effectiveness of the approach.
Conclusions:
- The developed method offers a robust solution for visual object tracking in challenging UAV environments.
- The combination of fused features, cellular automata, and a confidence-based updating strategy enhances tracking accuracy and stability.
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