Multisensor Data Fusion and Its Applications in Object Detection and Tracking
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".
Deadline for manuscript submissions: 20 April 2025 | Viewed by 25
Special Issue Editors
Interests: computer vision; image processing; pattern recognition; machine learning
Special Issues, Collections and Topics in MDPI journals
Interests: image processing; pattern recognition; data fusion; optical metrology
Special Issue Information
Dear Colleagues,
Multisensor data fusion integrates information from multiple sensors to improve object detection and tracking performance. By combining data from various sources, it mitigates limitations such as noise, blind spots, and environmental interference, making the system more reliable and accurate. Multisensor data fusion is widely applied in autonomous vehicles, robotics, surveillance, and aerospace, where precise and real-time object detection and tracking are critical. This technique leverages data obtained from different types of sensors, such as radar, LiDAR, cameras, and thermal sensors, to create a comprehensive understanding of the environment. Recent advancements in data fusion algorithms, machine learning, and signal processing have expanded the potential of multisensor data fusion, enabling the more efficient processing of sensor data and improving the robustness of object tracking systems. This Special Issue delves into innovative methodologies, frameworks, and real-world applications of multisensor data fusion, highlighting its significance in enhancing the detection and tracking capabilities of modern systems.
Prof. Dr. Huafeng Li
Dr. Xiaosong Li
Dr. Yafei Zhang
Guest Editors
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Keywords
- multisensor fusion
- multisource image fusion
- data fusion algorithms
- object detection
- object tracking
- autonomous systems
- machine learning
- sensor integration
- robotics perception
- signal processing for sensor fusion
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