Point Cloud Features-Based Kernel SVM for Human-Vehicle Classification in Millimeter Wave Radar

Zhao, Zihao; Song, Yuying; Cui, Fucheng; Zhu, Jiang; Song, Chunyi; Xu, Zhiwei; Ding, Kai

doi:10.1109/access.2020.2970533

Cited by 67 publications

(28 citation statements)

References 35 publications

(26 reference statements)

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“…Although FFT processes data of multiple dimensions, Range FFT data including range information and RDI including velocity information are calculated [ 33 ]. The frequency of IF signal is

, the range resolution is

, the maximum unambiguous velocity is

and the velocity resolution is

, where

is the wave length of the radar signal, and

is the number of chirps.…”

Section: Background Theory and Proposed Methodsmentioning

confidence: 99%

“…Radar point cloud data not only contains almost all the aforementioned features but also can directly indicate the spatial locations of the targets, and they are receiving more attention. However, most of these research investigations are focused only on feature extraction and recognition after obtaining point clouds without paying much attention to the generation of the point cloud [15,[32][33][34][35][36]. This causes inaccurate results because it is well known that the quality of the generated point cloud has a significant effect on the accuracy and effectiveness of the subsequent data process.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Comprehensive mPoint: A Method for 3D Point Cloud Generation of Human Bodies Utilizing FMCW MIMO mm-Wave Radar

Zhang

Geng

Lin

2021

Sensors

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In this paper, comprehensive mPoint, a method for generating 3D (range, azimuth, and elevation) point cloud of human targets using a Frequency-Modulated Continuous Wave (FMCW) signal and Multi-Input Multi-Output (MIMO) millimeter wave radar is proposed. Distinct from the TI-mPoint method proposed by TI technology, a comprehensive mPoint method considering both the static and dynamic characteristics of radar reflected signals is utilized to generate a high precision point cloud, resulting in more comprehensive information of the target being detected. The radar possessing 60–64 GHz FMCW signal with two sets of different dimensional antennas is utilized in order to experimentally verify the results of the methodology. By using the proposed process, the point cloud data of human targets can be obtained based on six different postures of the underlying human body. The human posture cube and point cloud accuracy rates are defined in the paper in order to quantitively and qualitatively evaluate the quality of the generated point cloud. Benefitting from the proposed comprehensive mPoint, evidence shows that the point number and the accuracy rate of the generated point cloud compared with those from the popular TI-mPoint can be largely increased by 86% and 42%, respectively. In addition, the noise level of multipath reflection can be effectively reduced. Moreover, the length of the algorithm running time is only 1.6% longer than that of the previous method as a slight tradeoff.

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“…Although FFT processes data of multiple dimensions, Range FFT data including range information and RDI including velocity information are calculated [ 33 ]. The frequency of IF signal is

, the range resolution is

, the maximum unambiguous velocity is

and the velocity resolution is

, where

is the wave length of the radar signal, and

is the number of chirps.…”

Section: Background Theory and Proposed Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comprehensive mPoint: A Method for 3D Point Cloud Generation of Human Bodies Utilizing FMCW MIMO mm-Wave Radar

Zhang

Geng

Lin

2021

Sensors

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show abstract

“…Therefore, the boundaries of the point clouds can be used as significant features for classification. The 2D point cloud boundary extraction methods include the rectangular box model [20], confidence ellipse model, and convex hull model [21]. Because the RADAR point cloud is distributed in 3D space, we used a 3D bounding box with orientation to extract the shape features, as shown in Figure 5.…”

Section: Proposed Classification Methodsmentioning

confidence: 99%

Traffic participants classification based on 3D radio detection and ranging point clouds

Bai

Tan

et al. 2021

IET Radar Sonar & Navi

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Traffic participant classification is critical in autonomous driving perception. Millimetre wave radio detection and ranging (RADAR) is a cost‐effective and robust means of performing this task in adverse traffic scenarios such as inclement weather (e.g. fog, snow, and rain) and poor lighting conditions. Compared to commercial two‐dimensional RADAR, the new generation of three‐dimensional (3D) RADAR can obtain height information about targets as well as their dense point clouds, greatly improving target classification capabilities. This study proposes a multi‐objective classification method for traffic participants based on 3D RADAR point clouds. First, a 22‐dimensional feature vector of the 3D RADAR point cloud distribution was extracted to describe the shape, discrete, Doppler, and reflection intensity features of the targets. Then, dynamic and static datasets containing five classes of targets were produced, creating a 10k frame. Extensive experiments were conducted to build machine learning classifiers. The experimental results show that the trained classifiers can achieve over 92% classification accuracy when the targets are classified into five groups and over 95% classification accuracy when the targets are classified into four groups. The proposed method can guide the design of safer and more efficient intelligent driving systems.

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“…Lorente et al [24] have shown that LIDAR and Time-of-flight (TOF) cameras can be used for point cloud acquisition and that such information can be, with the use of deep learning methods, applied for pedestrian detection. A millimeter-wave radar was used for a similar application, where Zhao et al [25] proposed a point cloud classification algorithm for human-vehicle classification in advanced driver assistance systems (ADAS). Aside from various imaging technologies, there are also examples of passive, nonimaging solutions for sensing a human presence.…”

Section: Pedestrian Detectionmentioning

confidence: 99%

Pedestrian Traffic Light Control with Crosswalk FMCW Radar and Group Tracking Algorithm

Nimac

Krpič²,

Batagelj

et al. 2022

Sensors

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The increased mobility requirements of modern lifestyles put more stress on existing traffic infrastructure, which causes reduced traffic flow, especially in peak traffic hours. This calls for new and advanced solutions in traffic flow regulation and management. One approach towards optimisation is a transition from static to dynamic traffic light intervals, especially in spots where pedestrian crossing cause stops in road traffic flow. In this paper, we propose a smart pedestrian traffic light triggering mechanism that uses a Frequency-modulated continuous-wave (FMCW) radar for pedestrian detection. Compared to, for example, camera-surveillance systems, radars have advantages in the ability to reliably detect pedestrians in low-visibility conditions and in maintaining privacy. Objects within a radar’s detection range are represented in a point cloud structure, in which pedestrians form clusters where they lose all identifiable features. Pedestrian detection and tracking are completed with a group tracking (GTRACK) algorithm that we modified to run on an external processor and not integrated into the used FMCW radar itself. The proposed prototype has been tested in multiple scenarios, where we focused on removing the call button from a conventional pedestrian traffic light. The prototype responded correctly in practically all cases by triggering the change in traffic signalization only when pedestrians were standing in the pavement area directly in front of the zebra crossing.

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Point Cloud Features-Based Kernel SVM for Human-Vehicle Classification in Millimeter Wave Radar

Cited by 67 publications

References 35 publications

Comprehensive mPoint: A Method for 3D Point Cloud Generation of Human Bodies Utilizing FMCW MIMO mm-Wave Radar

Comprehensive mPoint: A Method for 3D Point Cloud Generation of Human Bodies Utilizing FMCW MIMO mm-Wave Radar

Traffic participants classification based on 3D radio detection and ranging point clouds

Pedestrian Traffic Light Control with Crosswalk FMCW Radar and Group Tracking Algorithm

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