Millimeter-Wave Radar and RGB-D Camera Sensor Fusion for Real-Time People Detection and Tracking

Zewge, Natnael S.; Kim, Young‐Min; Kim, Jintae; Kim, Jong-Hwan

doi:10.1109/ritapp.2019.8932892

Cited by 12 publications

(3 citation statements)

References 8 publications

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“…Establishing accurate coordinate conversion relationship among millimeter wave radar coordinate system, three dimensional world coordinate system, camera coordinate system, image coordinate system and pixel coordinate system is the key to realize millimeter wave and vision fusion [7]. The spatial fusion of millimeter wave radar and vision sensor is to convert the measured values of different sensor coordinate systems to the same coordinate system.…”

Section: Disadvantages: the Longitudinal Distance Identified Bymentioning

confidence: 99%

P‐16.2: A Review of Obstacle Detection based on Multi‐sensor Fusion

Dai

2022

Symp Digest of Tech Papers

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According to the classification of using multiple sensors, the obstacle detection and recognition technology of unmanned driving is summarized, and the main technical means, algorithms, advantages and disadvantages, difficulties and so on are discussed. These methods mainly include lidar and camera fusion, camera and radar fusion and lidar, camera and radar fusion. In addition, some research achievements in this field in recent years are introduced, and some fusion technologies used in this field are briefly analyzed and summarized.

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Section: Disadvantages: the Longitudinal Distance Identified Bymentioning

confidence: 99%

P‐16.2: A Review of Obstacle Detection based on Multi‐sensor Fusion

Dai

2022

Symp Digest of Tech Papers

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“…However, compared with the other two sensors, there are limited large-scale and public datasets containing mmWave radar data. Moreover, due to the low resolution and high specularity of the mmWave radar, it is hard to obtain contextual or perceptual information and cannot directly detect the shape of an object (Zewge et al, 2019;Lee, 2020). MmWave radars have a relatively poor ability to identify objects compared with LiDARs and cameras.…”

Section: D Object Detection Through Single Sensor Modalitymentioning

confidence: 99%

Multi-Modal 3D Object Detection in Autonomous Driving: a Survey

Wang

Mao

Zhu

et al. 2021

Preprint

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In the past few years, we have witnessed rapid development of autonomous driving. However, achieving full autonomy remains a daunting task due to the complex and dynamic driving environment. As a result, self-driving cars are equipped with a suite of sensors to conduct robust and accurate environment perception. As the number and type of sensors keep increasing, combining them for better perception is becoming a natural trend. So far, there has been no indepth review that focuses on multi-sensor fusion based perception. To bridge this gap and motivate future research, this survey devotes to review recent fusion-based *equal contribution

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“…Autonomous cars [1], unmanned aerial vehicles [2], and intelligent robots [3] are usually equipped for target detection and target classification with a variety of sensors, such as cameras, radar (radio detection and ranging), laser radar, etc. [4]. In the past, the dominant approach was to obtain object recognition data using a single sensor.…”

Section: Introductionmentioning

confidence: 99%

Research of Target Detection and Classification Techniques Using Millimeter-Wave Radar and Vision Sensors

et al. 2021

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The development of autonomous vehicles and unmanned aerial vehicles has led to a current research focus on improving the environmental perception of automation equipment. The unmanned platform detects its surroundings and then makes a decision based on environmental information. The major challenge of environmental perception is to detect and classify objects precisely; thus, it is necessary to perform fusion of different heterogeneous data to achieve complementary advantages. In this paper, a robust object detection and classification algorithm based on millimeter-wave (MMW) radar and camera fusion is proposed. The corresponding regions of interest (ROIs) are accurately calculated from the approximate position of the target detected by radar and cameras. A joint classification network is used to extract micro-Doppler features from the time-frequency spectrum and texture features from images in the ROIs. A fusion dataset between radar and camera is established using a fusion data acquisition platform and includes intersections, highways, roads, and playgrounds in schools during the day and at night. The traditional radar signal algorithm, the Faster R-CNN model and our proposed fusion network model, called RCF-Faster R-CNN, are evaluated in this dataset. The experimental results indicate that the mAP(mean Average Precision) of our network is up to 89.42% more accurate than the traditional radar signal algorithm and up to 32.76% higher than Faster R-CNN, especially in the environment of low light and strong electromagnetic clutter.

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Millimeter-Wave Radar and RGB-D Camera Sensor Fusion for Real-Time People Detection and Tracking

Cited by 12 publications

References 8 publications

P‐16.2: A Review of Obstacle Detection based on Multi‐sensor Fusion

P‐16.2: A Review of Obstacle Detection based on Multi‐sensor Fusion

Multi-Modal 3D Object Detection in Autonomous Driving: a Survey

Research of Target Detection and Classification Techniques Using Millimeter-Wave Radar and Vision Sensors

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