Journal ArticleDOI
Robust Detection and Tracking Method for Moving Object Based on Radar and Camera Data Fusion
TLDR
In this article, the authors developed a robust multi-object detection and tracking method for moving objects based on radar and camera data fusion, which can accurately estimate the number and state of targets in object occlusion, measurement loss scenarios, and achieve robust continuous multiobject tracking.Abstract:
Obtaining the accurate and real-time state of surrounding objects is essential for automated vehicle planning and decision-making to ensure safe driving. In complex traffic scenarios, object occlusion, clutter interference, and limited sensor detection capabilities lead to false alarms and missed object detection, making it challenging to ensure the stability of tracking and state prediction. To address these challenges, in this study, we developed a robust multi-object detection and tracking method for moving objects based on radar and camera data fusion. First, the radar and camera perform target detection independently, and the detection results are correlated in the image plane to generate a random finite set with an object type. Then, based on the Gaussian mixture probability hypothesis density algorithm framework, the tracking process is improved using elliptic discriminant thresholds, an attenuation function, and simplified pruning methods. The experimental results demonstrate that the improved algorithm can accurately estimate the number and state of targets in object occlusion, measurement loss scenarios, and achieve robust continuous multi-object tracking. The proposed method could guide the design of safer and more efficient intelligent driving systems.read more
Citations
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Journal ArticleDOI
Scene-adaptive radar tracking with deep reinforcement learning
Michael Stephan,Lorenzo Servadei,Jose A. Arjona-Medina,Avik Santra,Robert Wille,Georg Fischer +5 more
TL;DR: In this paper , a Deep Reinforcement Learning framework is proposed to guide the scene-adaptive choice of radar tracking-parameters towards an improved performance on multi-target tracking.
Journal ArticleDOI
A Velocity Estimation Technique for a Monocular Camera Using mmWave FMCW Radars
TL;DR: A complete implementation of camera–mmW radar late feature fusion to improve the camera’s velocity estimation performance is presented, implementing a lightweight ML model that successfully maps the mmW radar features to the camera, allowing it to perceive and estimate the dynamics of a target object without any calibration.
Proceedings ArticleDOI
Intruder Detection and Tracking using 77GHz FMCW Radar and Camera Data
TL;DR: The experimental result shows that combining radar and optical sensors accomplishes tracking accuracy and coherence in target detection and tracking.
Journal ArticleDOI
Object Detection Based on Roadside LiDAR for Cooperative Driving Automation: A Review
TL;DR: In this paper , LiDAR is placed at the commanding height of the traffic scene, the overall situation can be grasped from the perspective of top view, and the trajectory of each object can be accurately perceived in real time.
Journal ArticleDOI
Adaptive Multi-Pedestrian Tracking by Multi-Sensor: Track-to-Track Fusion Using Monocular 3D Detection and MMW Radar
Yipeng Zhu,Shi-peng Zhu +1 more
TL;DR: Wang et al. as discussed by the authors proposed a novel track-to-track fusion strategy for multi-pedestrian tracking by using a millimeter-wave (MMW) radar and a monocular camera.
References
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You Only Look Once: Unified, Real-Time Object Detection
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Book ChapterDOI
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Proceedings ArticleDOI
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Posted Content
YOLOv3: An Incremental Improvement.
Joseph Redmon,Ali Farhadi +1 more
TL;DR: The authors present some updates to YOLO!
Proceedings ArticleDOI
Focal Loss for Dense Object Detection
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