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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


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Guest Editor
Computer Science Department, Technical University of Cluj-Napoca, Str. Memorandumului, Nr. 28 , 400 114 Cluj Napoca, Romania
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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Published Papers (1 paper)

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Research

24 pages, 4426 KB  
Article
Occlusion-Aware Trajectory Discontinuity Correction for Roadside LiDAR Using Time–Space Analysis
by Mingshu Dong, Hao Xu, Muchen Tian, Fei Guan, Ziru Wang, Renjuan Sun and Yanhua Guan
Sensors 2026, 26(12), 3755; https://doi.org/10.3390/s26123755 (registering DOI) - 12 Jun 2026
Abstract
Recent advances in roadside sensing technologies, including camera-based systems, radar, and LiDAR, have enabled high-resolution sampling of vehicle trajectories, overcoming the temporal and spatial limitations of traditional data collection methods. Among these, LiDAR sensing has been widely adopted for traffic monitoring and surrogate [...] Read more.
Recent advances in roadside sensing technologies, including camera-based systems, radar, and LiDAR, have enabled high-resolution sampling of vehicle trajectories, overcoming the temporal and spatial limitations of traditional data collection methods. Among these, LiDAR sensing has been widely adopted for traffic monitoring and surrogate safety analysis due to its high spatial accuracy and temporal resolution. However, sensor noise and occlusion in roadside LiDAR frequently introduce tracking point offsets and trajectory discontinuities, reducing the reliability of vehicle counts, traffic state estimation, and conflict analysis. To address these challenges, this study proposes a post-processing method based on time–space analysis to detect and correct occlusion-induced trajectory discontinuities. By exploiting the inherent spatiotemporal consistency of vehicle movements, the proposed approach identifies fragmented trajectories, reconstructs continuous vehicle paths, and recovers realistic traffic patterns. Validated on real-world LiDAR data collected at an urban intersection in Reno, Nevada, across four 30 min traffic periods covering AM and PM peak conditions on weekdays and weekends, the proposed method achieves an average precision of 0.989 and an average F1-score of 0.948, outperforming IMM, GNN-RM, and HMM + Viterbi benchmark methods. Count accuracy improved from 85.5% to 97.4% across all evaluated periods, confirming the method’s effectiveness under occlusion conditions. Full article
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