Pedestrian Recognition Based on 24 GHz Radar Sensors

Heuel, Steffen; Rohling, Hermann

doi:10.5772/53007

“…Even though CNNs function extraordinarily well on images, they can also be tried and applied to other sensors that can yield image-like data [ 108 ]. The two-dimensional radar grid representations accumulated according to different occupancy grid map algorithms have already been exploited in deep learning domains for various autonomous system tasks, such as static object classification [ 109 , 110 , 111 , 112 , 113 , 114 ] and dynamic object classification [ 115 , 116 , 117 ]. In this case, the objects denote any road user within an autonomous system environment, like the pedestrian, vehicles, motorcyclists, etc.…”

Section: Detection and Classification Of Radar Signals Using Deep mentioning

confidence: 99%

“…Some authors, like [ 115 , 116 ], applied feature-based methods for classification. Schumann et al [ 117 ] utilized a random forest classifier and long short-term memory (LSTM) based on radar data to classify dynamically moving objects.…”

Section: Detection and Classification Of Radar Signals Using Deep mentioning

confidence: 99%

Application of Deep Learning on Millimeter-Wave Radar Signals: A Review

Abdu

¹

,

Zhang

²

,

Fu

³

et al. 2021

Sensors

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The progress brought by the deep learning technology over the last decade has inspired many research domains, such as radar signal processing, speech and audio recognition, etc., to apply it to their respective problems. Most of the prominent deep learning models exploit data representations acquired with either Lidar or camera sensors, leaving automotive radars rarely used. This is despite the vital potential of radars in adverse weather conditions, as well as their ability to simultaneously measure an object’s range and radial velocity seamlessly. As radar signals have not been exploited very much so far, there is a lack of available benchmark data. However, recently, there has been a lot of interest in applying radar data as input to various deep learning algorithms, as more datasets are being provided. To this end, this paper presents a survey of various deep learning approaches processing radar signals to accomplish some significant tasks in an autonomous driving application, such as detection and classification. We have itemized the review based on different radar signal representations, as it is one of the critical aspects while using radar data with deep learning models. Furthermore, we give an extensive review of the recent deep learning-based multi-sensor fusion models exploiting radar signals and camera images for object detection tasks. We then provide a summary of the available datasets containing radar data. Finally, we discuss the gaps and important innovations in the reviewed papers and highlight some possible future research prospects.

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“…The paper proposed two systems such as single radar system, that measures the transmitted signal using a single MFSK (Multi-Frequency Shift Keying) at 39 ms. It is used to measure the radial speed, range and the level of signal [8]. In the second system named as, multiple radar system wherein measurements are modified using different speed and range of the signal.…”

Section: Related Workmentioning

confidence: 99%

A real-time pedestrian detection system for safety applications

John¹

2021

Preprint

0

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Nowadays, People are more distracted by their vulnerable devices, whenever they enter a cross road. As a result, a fatal accident or injury will occur. This motivated the need to implement a reliable pedestrian detection system. To optimize the system, a cross road scenario is considered where the driver is taking a right turn and a smart camera is used to capture consecutive pictures of the pedestrian. The consecutive frames are studied using Region Of Interest method and the Gaussian mixture model method. Once the detected pedestrian enters region of interest in less than 2 meters, a warning and automatic brake system is initiated to prevent the accident. Finally, the results of the proposed methods are compared based on the processing speed and performance rate of the Shape based detection technique (Wei Zhang, [12]). The performance rate was above 90% and processing speed was about 1 sec for the proposed methods.

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“…Steffen Heuel and Hermann Rohling developed a classification algorithm for automotive application using radar sensors (at 24GHz), which can be used for measuring velocity and distance with a band-width of 150MHz [8]. The paper proposed two systems such as single radar system, that measures the transmitted signal using a single MFSK (Multi-Frequency Shift Keying) at 39 ms.…”

Section: Related Workmentioning

confidence: 99%

A real-time pedestrian detection system for safety applications

John¹

2021

Preprint

0

View full text Add to dashboard Cite

Nowadays, People are more distracted by their vulnerable devices, whenever they enter a cross road. As a result, a fatal accident or injury will occur. This motivated the need to implement a reliable pedestrian detection system. To optimize the system, a cross road scenario is considered where the driver is taking a right turn and a smart camera is used to capture consecutive pictures of the pedestrian. The consecutive frames are studied using Region Of Interest method and the Gaussian mixture model method. Once the detected pedestrian enters region of interest in less than 2 meters, a warning and automatic brake system is initiated to prevent the accident. Finally, the results of the proposed methods are compared based on the processing speed and performance rate of the Shape based detection technique (Wei Zhang, [12]). The performance rate was above 90% and processing speed was about 1 sec for the proposed methods.

show abstract

Pedestrian Recognition Based on 24 GHz Radar Sensors

Cited by 18 publications

References 13 publications

Application of Deep Learning on Millimeter-Wave Radar Signals: A Review

Application of Deep Learning on Millimeter-Wave Radar Signals: A Review

A real-time pedestrian detection system for safety applications

A real-time pedestrian detection system for safety applications

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