Optical, Radar and Lidar Sensing Technologies for Object Detection, Recognition and Tracking
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".
Deadline for manuscript submissions: 25 January 2027 | Viewed by 147
Special Issue Editor
Interests: vehicle detection; vehicle speed; real-time image processing techniques; stereovision-based object recognition; 3D lane detection from moving vehicles; object tracking; stereo vision; tracking; environment modeling; space surveillance
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Special Issue Information
Dear Colleagues,
Many applications, from security surveillance to autonomous vehicles, and from medical diagnosis to space surveillance, rely on accurate perception of the target environment. Today, we use a large variety of sensors, the most popular being cameras, with their rich data output, lidars, with their extremely accurate distance measurement capabilities, and radars, with their speed estimation capabilities. The data from these sensors can be processed independently, or the sensors can be combined to develop multi-modal sensing systems. The algorithms used to process sensor data are constantly evolving, mostly driven by advances in machine learning techniques. Still, challenges remain: 3D environments are complex and dynamic, objects can occlude other objects, visibility conditions can be poor, leading to a low signal-to-noise ratio, and, depending on the desired application, hardware resources can be limited.
This Special Issue aims to highlight recent advances in multi-modal sensing systems. Topics include but are not limited to:
- Advanced sensing techniques: Algorithm-based and machine learning-based processing of sensor data, including noise reduction, feature extraction, segmentation, 3D reconstruction, object detection, and object tracking.
- Multi-Modal Sensor Fusion: Combining the strengths of individual sensors (the camera, lidar, radar, etc.) for improved environment sensing and understanding, SLAM (Simultaneous Localization and Mapping), and navigation.
- Perception Applications: Sensing for autonomous vehicles, drones, robotics, biomedical imaging, space surveillance, etc.
- Sensing using limited computation resources: Design and optimization of sensing technologies and algorithms for operation on edge computing platforms, reducing energy and computing power requirements.
Prof. Dr. Radu Danescu
Guest Editor
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Keywords
- multi-modal sensor fusion
- object detection and tracking
- machine learning for perception
- autonomous navigation
- 3D environment perception
- Simultaneous Localization and Mapping (SLAM)
- edge computing for sensing
- radar/LiDAR/camera signal processing
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