HSI COLOR CONVERSION AND HOG FEATURE EXTRACTION IN TRAFFIC LIGHT DETECTION USING AdaBSF

Traffic light detection and recognition (TLR) research has grown every year. In addition, Machine Learning (ML) has been largely used not only in traffic light research but in every field where it is useful and possible to generalize data and automatize human behavior. ML algorithms require a large amount of data to work properly and, thus, a lot of computational power is required to analyze the data. We argue that expert knowledge should be used to decrease the burden of collecting a huge amount of data for ML tasks. In this paper, we show how such kind of knowledge was used to reduce the amount of data and improve the accuracy rate for traffic light detection and recognition. Results show an improvement in the accuracy rate around 15%. The paper also proposes a TLR device prototype using both camera and processing unit of a smartphone which can be used as a driver assistance. To validate such layout prototype, a dataset was built and used to test an ML model based on adaptive background suppression filter (AdaBSF) and Support Vector Machines (SVMs). Results show 100% precision rate and recall of 65%.

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Feature Extraction from Light Detection and Ranging Data in Urban Areas

Topographic Laser Ranging and Scanning ◽

10.1201/9781315154381-14 ◽

2018 ◽

pp. 443-458

Author(s):

Frédéric Bretar

Keyword(s):

Feature Extraction ◽

Urban Areas ◽

Light Detection And Ranging ◽

Light Detection

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A Novel Method for Feature Extraction and Classification for an Aerial Image and LiDAR Data with Genetic Algorithm

International Journal of Computer Science and Informatics ◽

10.47893/ijcsi.2012.1050 ◽

2012 ◽

pp. 264-270

Author(s):

Manjunath B. E ◽

D. G. Anand ◽

Mahant. G. Kattimani

Keyword(s):

Feature Extraction ◽

Light Detection And Ranging ◽

Aerial Image ◽

Lidar Data ◽

Light Detection ◽

Aerial Photos ◽

The Earth ◽

Digital Elevation ◽

Land Surveying ◽

Novel Method

Airborne Light Detection and Ranging (LiDAR) provides accurate height information for objects on the earth, which makes LiDAR become more and more popular in terrain and land surveying. In particular, LiDAR data offer vital and significant features for land-cover classification which is an important task in many application domains. Aerial photos with LiDAR data were processed with genetic algorithms not only for feature extraction but also for orthographical image. DSM provided by LiDAR reduced the amount of GCPs needed for the regular processing, thus the reason both efficiency and accuracy are highly improved. LiDAR is an acronym for Light Detection and Ranging, which is typically defined as an integration of three technologies into a single system, which is capable of acquiring a data to produce accurate Digital Elevation Models.

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TRAFFIC LIGHT DETECTION USING CONIC SECTION GEOMETRY

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iii-1-191-2016 ◽

2016 ◽

Vol III-1 ◽

pp. 191-200

Author(s):

S. Hosseinyalmdary ◽

A. Yilmaz

Keyword(s):

Prior Knowledge ◽

Pose Estimation ◽

Open Problem ◽

Autonomous Vehicles ◽

Conic Section ◽

Traffic Light ◽

Light Detection ◽

State Recognition ◽

Traffic Lights ◽

Stereo Cameras

Traffic lights detection and their state recognition is a crucial task that autonomous vehicles must reliably fulfill. Despite scientific endeavors, it still is an open problem due to the variations of traffic lights and their perception in image form. Unlike previous studies, this paper investigates the use of inaccurate and publicly available GIS databases such as OpenStreetMap. In addition, we are the first to exploit conic section geometry to improve the shape cue of the traffic lights in images. Conic section also enables us to estimate the pose of the traffic lights with respect to the camera. Our approach can detect multiple traffic lights in the scene, it also is able to detect the traffic lights in the absence of prior knowledge, and detect the traffics lights as far as 70 meters. The proposed approach has been evaluated for different scenarios and the results show that the use of stereo cameras significantly improves the accuracy of the traffic lights detection and pose estimation.

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