Vehicle classification in congested traffic based on 3D point cloud using SVM and KNN

Raktrakulthum, Porn-anan; Netramai, Chayakorn

doi:10.1109/iciteed.2017.8250451

Cited by 12 publications

(11 citation statements)

References 6 publications

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“…Many wireless video streaming systems have been developed, but most do not provide real-time video streaming. IoVT based video streaming solutions suitable for real-time traffic flow characterization are fewer still [18], [23]. Raktrakulthum et al [18] used machine learning for vehicle classification under low, moderate, and high traffic densities.…”

Section: Related Workmentioning

confidence: 99%

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Internet-of-Video Things Based Real-Time Traffic Flow Characterization

Khan¹,

Khattak²,

Khan³

et al. 2021

ICST Transactions on Scalable Information Systems

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Real-world traffic flow parameters are fundamental for devising smart mobility solutions. Though numerous solutions (intrusive and non-intrusive sensors) have been proposed, however, these have serious limitations under heterogeneous and congested traffic conditions. To overcome these limitations, a low-cost real-time Internet-of-Video-Things solution has been proposed. The sensor node (fabricated using Raspberry Pi 3B, Pi cameral and power bank) has the capability to stream 2 Mbps MJPEG video of 640x480 resolution and 20 frames per second (fps). The Camlytics traffic analysis software installed on a Dell desktop is employed for traffic flow characterization. The proposed solution was field-tested with vehicle detection rate of 85.3%. The novelty of the proposed system is that in addition to vehicle count, it has the capability to measure speed, density, time headway, time-space diagram and trajectories. Obtained results can be employed for road network planning, designing and management.

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Section: Related Workmentioning

confidence: 99%

“…However, these solutions are constrained by the computational limitations of single board computers (SBC) such as the Raspberry Pi (RPi). Thus, they are only able to provide vehicle count [14]- [16], vehicle count, and average speed [17], or vehicle count and type [18]- [21].…”

Section: Introductionmentioning

confidence: 99%

Internet-of-Video Things Based Real-Time Traffic Flow Characterization

Khan¹,

Khattak²,

Khan³

et al. 2021

ICST Transactions on Scalable Information Systems

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“…The SA module consists of a sampling layer, a grouping layer, and a feature extraction layer. The sampling layer samples the point clouds globally by the farthest point sampling method; the grouping layer constructs multiple local regions by the K-nearest neighbor algorithm [29] or by specifying the radius of the sampled points; the feature extraction layer extracts the sampled and grouped point clouds using MLP to increase the feature dimension.…”

Section: Spatial Eight-quadrant Kernel Convolution Algorithm-based Networkmentioning

confidence: 99%

Semantic Segmentation of 3D Point Cloud Based on Spatial Eight-Quadrant Kernel Convolution

Liu

Tan

et al. 2021

Remote Sensing

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In order to deal with the problem that some existing semantic segmentation networks for 3D point clouds generally have poor performance on small objects, a Spatial Eight-Quadrant Kernel Convolution (SEQKC) algorithm is proposed to enhance the ability of the network for extracting fine-grained features from 3D point clouds. As a result, the semantic segmentation accuracy of small objects in indoor scenes can be improved. To be specific, in the spherical space of the point cloud neighborhoods, a kernel point with attached weights is constructed in each octant, the distances between the kernel point and the points in its neighborhood are calculated, and the distance and the kernel points’ weights are used together to weight the point cloud features in the neighborhood space. In this case, the relationship between points are modeled, so that the local fine-grained features of the point clouds can be extracted by the SEQKC. Based on the SEQKC, we design a downsampling module for point clouds, and embed it into classical semantic segmentation networks (PointNet++, PointSIFT and PointConv) for semantic segmentation. Experimental results on benchmark dataset ScanNet V2 show that SEQKC-based PointNet++, PointSIFT and PointConv outperform the original networks about 1.35–2.12% in terms of MIoU, and they effectively improve the semantic segmentation performance of the networks for small objects of indoor scenes, e.g., the segmentation accuracy of small object “picture” is improved from 0.70% of PointNet++ to 10.37% of SEQKC-PointNet++.

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“…If the k-value is too small, it will be too sensitive to noise. If the k-value is too large, the neighborhood may also include other classes [20]. ED is a weighted feature value.…”

Section: Ed = Euclidean Distance = Test Data = Training Data = Numbermentioning

confidence: 99%

HOG Feature Extraction and KNN Classification for Detecting Vehicle in The Highway

Putra

Utaminingrum

Mahmudy

2020

Indonesian J. Comput. Cybern. Syst.

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Autonomous car is a vehicle that can guide itself without human intervention. Various types of rudderless vehicles are being developed. Future systems where computers take over the art of driving. The problem is prior to being attention in an autonomous car for obtaining the high safety. Autonomous car need early warning system to avoid accidents in front of the car, especially the system can be used in the Highway location. In this paper, we propose a vision-based vehicle detection system for Autonomous car. Our detection algorithm consists of three main components: HOG feature extraction, KNN classifier, and vehicle detection. Feature extraction has been used to recognize an object such as cars. In this case, we use HOG feature extraction to detect as a car or non-car. We use the KNN algorithm to classify. KNN Classification in previous studies had quite good results. Car detected by matching about trining data with testing data. Trining data created by extract HOG feature from image 304 x 240 pixels. The system will produce a classification between car or non-car.

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Vehicle classification in congested traffic based on 3D point cloud using SVM and KNN

Cited by 12 publications

References 6 publications

Internet-of-Video Things Based Real-Time Traffic Flow Characterization

Internet-of-Video Things Based Real-Time Traffic Flow Characterization

Semantic Segmentation of 3D Point Cloud Based on Spatial Eight-Quadrant Kernel Convolution

HOG Feature Extraction and KNN Classification for Detecting Vehicle in The Highway

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