Robust Detection and Tracking Method for Moving Object Based on Radar and Camera Data Fusion

Bai, Jie; Li, Sen; Huang, Libo; Chen, Huanlei

doi:10.1109/jsen.2021.3049449

Cited by 55 publications

(15 citation statements)

References 33 publications

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“…The electronic control unit in the sensor causes the circuit to excite the Piezoelectric transducer, which causes the membrane attached to vibrate and emit pings. Ultrasonic sensors cannot detect objects which are in very close proximity as they cannot receive signals before the ring downtime which is approximately 700 µs [27]. The ultrasonic sensors detect objects around the car and that information helps in automatically switching lanes.…”

Section: Automobile Sensorsmentioning

confidence: 99%

Emergency Rapid Response to Epileptic Seizures - A Novel IOT Framework for Smart Cities

Ziyad

Ziyad²

2021

SCPE

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Epilepsy is a common neurological disorder that results in seizures in patients of all ages. The frequency of seizure episodes can be controlled by prescribing anti-seizure drugs. Drug-resistant epilepsy is a condition where the seizures are uncontrolled by strong medications. Such patients are at a high risk of getting seizures frequently and prone to injuries due to sudden falls. Many countries prohibit epileptic patients from driving as sudden seizure attacks can cause loss of lives and property. In the past decades immense work has been carried out in the to monitor the seizure activity in patients and alert caregivers to extend help in emergencies. The study proposes a smart health care Internet of things framework to provide immediate help to the epileptic patient during an episode while travelling in a self-driving car. In the proposed framework, the seizure alert from a wearable device of the patient is transmitted to the control application via cloud. The control application also receives data from the vision, ultrasonic, and radar sensors. The critical information of seizure alert and the sensor data commands the car to force stop. The seizure google map location of the car is sent to the patient’s caregiver as well as the registered hospital. Many applications are being developed to provide luxury and comfort to fully automated car drivers. Far from providing luxury to the driver Emergency Rapid Response to Epileptic Seizure aims to propose a solution that could save the life of an epileptic patient who is drug-resistant or prone to frequent attacks despite severe medications. The experimental results on synchonization of clouds show that the minimum time is 30 sec 30 ms and maximum time is 31 sec 63 ms. The experimental results prove that its recommended to alert the patient's caregiver directly from control application rather than alerting via cloud.

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Section: Automobile Sensorsmentioning

confidence: 99%

Emergency Rapid Response to Epileptic Seizures - A Novel IOT Framework for Smart Cities

Ziyad

Ziyad²

2021

SCPE

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“…Bai et al performed independent target detection using radar and cameras and associated the detection results within the image plane to form a random finite set with object-type labels. Subsequently, they refined the tracking process within the framework of a Gaussian mixture probability hypothesis density algorithm using an elliptical gating threshold, a decay function, and a simplified pruning method [13].…”

Section: Introductionmentioning

confidence: 99%

Radar-optical fusion detection of UAV based on improved YOLOv7-tiny

Tang,

Xiong,

Dong

et al. 2024

Meas. Sci. Technol.

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This study presents a radar-optical fusion detection method for unmanned aerial vehicles (UAVs) in maritime environments. Radar and camera technologies are integrated to improve the detection capabilities of the platforms. The proposed method involves generating Regions of Interest (ROI) by projecting radar traces onto optical images through matrix transformation and geometric centroid registration. The generated ROI are matched with YOLO detection boxes using the intersection-over-union (IoU) algorithm, enabling radar-optical fusion detection. A modified algorithm, called SPN-YOLOv7-tiny, is developed to address the challenge of detecting small UAV targets that are easily missed in images. In this algorithm, the convolutional layers in the backbone network are replaced with a space-to-depth convolution (SPD-Conv), and a small object detection layer is added. In addition, the loss function was replaced with a NWD (Normalized weighted Distance) loss function. Experimental results demonstrate that compared to the original YOLOv7-tiny method, SPN-YOLOv7-tiny achieves an improved mAP@0.5 (mean average precision at an IoU threshold of 0.5) from 0.852 to 0.93, while maintaining a high frame rate of 135.1 frames per second. Moreover, the proposed radar-optical fusion detection method achieves an accuracy of 96.98%, surpassing the individual detection results of the radar and camera. The proposed method effectively addresses the detection challenges posed by closely spaced overlapping targets on a radar chart.

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“…At present, the intelligent roadside perception system mainly relies on traditional traffic information collectors, such as radar [ 5 , 6 ], cameras [ 7 , 8 , 9 ], and their fusion [ 10 , 11 , 12 , 13 ]. Radar can only capture moving objects in a certain area with low accuracy.…”

Section: Introductionmentioning

confidence: 99%

Object Detection Based on Roadside LiDAR for Cooperative Driving Automation: A Review

Sun

Wang

et al. 2022

Sensors

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Light Detection and Ranging (LiDAR) technology has the advantages of high detection accuracy, a wide range of perception, and not being affected by light. The 3D 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 in the traffic scene can be accurately perceived in real time, and then the object information can be distributed to the surrounding vehicles or other roadside LiDAR through advanced wireless communication equipment, which can significantly improve the local perception ability of an autonomous vehicle. This paper first describes the characteristics of roadside LiDAR and the challenges of object detection and then reviews in detail the current methods of object detection based on a single roadside LiDAR and multi-LiDAR cooperatives. Then, some studies for roadside LiDAR perception in adverse weather and datasets released in recent years are introduced. Finally, some current open challenges and future works for roadside LiDAR perception are discussed. To the best of our knowledge, this is the first work to systematically study roadside LiDAR perception methods and datasets. It has an important guiding role in further promoting the research of roadside LiDAR perception for practical applications.

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Robust Detection and Tracking Method for Moving Object Based on Radar and Camera Data Fusion

Cited by 55 publications

References 33 publications

Emergency Rapid Response to Epileptic Seizures - A Novel IOT Framework for Smart Cities

Emergency Rapid Response to Epileptic Seizures - A Novel IOT Framework for Smart Cities

Radar-optical fusion detection of UAV based on improved YOLOv7-tiny

Object Detection Based on Roadside LiDAR for Cooperative Driving Automation: A Review

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