Towards Autonomous Driving: A Machine Learning-based Pedestrian Detection System using 16-Layer LiDAR

The frequency of pedestrian traffic accidents continues to increase in Japan. Thus, a driver assistance system is expected to reduce the number of accidents. However, it is difficult for the current environmental recognition sensors to detect crossing pedestrians when turning at intersections, owing to the field of view and the cost. We propose a pedestrian detection system that utilizes surround-view fisheye cameras with a wide field of view. The system can be realized at low cost if the fisheye cameras are already equipped. It is necessary to detect the positioning of pedestrians accurately because more precise prediction of future collision points is required at intersections. Stereo vision is suitable for this purpose. However, there are some concerns regarding realizing stereo vision using fisheye cameras due to the distortion of the lens, asynchronous capturing, and fluctuating camera postures. As a countermeasure, we propose a novel method combining various machine-learning techniques. The D-Brief with histogram of oriented gradients and normalized cross-correlation are combined by a support-vector machine for stereo matching. A random forest was adopted to discriminate the pedestrians from noise in the 3D reconstructed point cloud. We evaluated this for images of crossing pedestrians at actual intersections. A tracking rate of 96.0% was achieved as the evaluation result. It was verified that this algorithm can accurately detect a pedestrian with an average position error of 0.17 m.

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A High Accuracy Pedestrian Detection System Combining a Cascade AdaBoost Detector and Random Vector Functional-Link Net

The Scientific World JOURNAL ◽

10.1155/2014/105089 ◽

2014 ◽

Vol 2014 ◽

pp. 1-7 ◽

Cited By ~ 9

Author(s):

Zhihui Wang ◽

Sook Yoon ◽

Shan Juan Xie ◽

Yu Lu ◽

Dong Sun Park

Keyword(s):

Machine Learning ◽

Random Vector ◽

Detection System ◽

Pedestrian Detection ◽

High Accuracy ◽

Machine Learning Algorithms ◽

Detection Methods ◽

Functional Link ◽

Detection Rates ◽

Standard Scale

In pedestrian detection methods, their high accuracy detection rates are always obtained at the cost of a large amount of false pedestrians. In order to overcome this problem, the authors propose an accurate pedestrian detection system based on two machine learning methods: cascade AdaBoost detector and random vector functional-link net. During the offline training phase, the parameters of a cascade AdaBoost detector and random vector functional-link net are trained by standard dataset. These candidates, extracted by the strategy of a multiscale sliding window, are normalized to be standard scale and verified by the cascade AdaBoost detector and random vector functional-link net on the online phase. Only those candidates with high confidence can pass the validation. The proposed system is more accurate than other single machine learning algorithms with fewer false pedestrians, which has been confirmed in simulation experiment on four datasets.

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Machine Learning-Based Automatic Detection System for Demarcation Line of Gastric Cancer with NBI Images

10.26226/morressier.59a6b342d462b80290b541b0 ◽

2017 ◽

Author(s):

Naoya Ishida

Keyword(s):

Machine Learning ◽

Gastric Cancer ◽

Detection System ◽

Automatic Detection ◽

Demarcation Line ◽

Automatic Detection System

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Inappropriate thermal image detection system using machine learning

Journal of rehabilitation welfare engineering & assistive technology ◽

10.21288/resko.2020.14.2.102 ◽

2020 ◽

Vol 14 (2) ◽

pp. 102-111

Author(s):

S. W. Kwon ◽

I. S. Song ◽

S. W. Lee ◽

J. S. Lee ◽

J. H. Kim ◽

...

Keyword(s):

Machine Learning ◽

Detection System ◽

Thermal Image ◽

Image Detection

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Anomaly Activities Detection System in Critical Water SCADA Infrastructure Using Machine Learning Techniques

Menoufia Journal of Electronic Engineering Research ◽

10.21608/mjeer.2019.69027 ◽

2019 ◽

Vol 28 (1) ◽

pp. 343-384 ◽

Cited By ~ 1

Author(s):

Gamal Eldin I. Selim ◽

EZZ El-Din Hemdan ◽

Ahmed M. Shehata ◽

Nawal A. El-Fishawy

Keyword(s):

Machine Learning ◽

Detection System ◽

Machine Learning Techniques ◽

Learning Techniques

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Supervised Classifier Approach for Intrusion Detection on KDD with Optimal MapReduce Framework Model in Cloud Computing

Recent Patents on Computer Science ◽

10.2174/1573401315666190619113510 ◽

2019 ◽

Vol 12 ◽

Author(s):

M. Ilayaraja ◽

S. Hemalatha ◽

P. Manickam ◽

K. Sathesh Kumar ◽

K. Shankar

Keyword(s):

Machine Learning ◽

Cloud Computing ◽

Intrusion Detection ◽

Decision Tree ◽

Learning Strategies ◽

Nearest Neighbor ◽

Detection System ◽

K Nearest Neighbor ◽

Mapreduce Model ◽

The Web

Cloud computing is characterized as the arrangement of assets or administrations accessible through the web to the clients on their request by cloud providers. It communicates everything as administrations over the web in view of the client request, for example operating system, organize equipment, storage, assets, and software. Nowadays, Intrusion Detection System (IDS) plays a powerful system, which deals with the influence of experts to get actions when the system is hacked under some intrusions. Most intrusion detection frameworks are created in light of machine learning strategies. Since the datasets, this utilized as a part of intrusion detection is Knowledge Discovery in Database (KDD). In this paper detect or classify the intruded data utilizing Machine Learning (ML) with the MapReduce model. The primary face considers Hadoop MapReduce model to reduce the extent of database ideal weight decided for reducer model and second stage utilizing Decision Tree (DT) classifier to detect the data. This DT classifier comprises utilizing an appropriate classifier to decide the class labels for the non-homogeneous leaf nodes. The decision tree fragment gives a coarse section profile while the leaf level classifier can give data about the qualities that influence the label inside a portion. From the proposed result accuracy for detection is 96.21% contrasted with existing classifiers, for example, Neural Network (NN), Naive Bayes (NB) and K Nearest Neighbor (KNN).

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