Open Access Theses and Dissertations

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You searched for subject:(Traffic Light recognition). Showing records 1 – 3 of 3 total matches.

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University of Illinois – Chicago

1. Manavella, Andrea. Traffic Light Detection for Portable Assistive Device to Aid Blind Pedestrians.

Degree: 2015, University of Illinois – Chicago

URL: http://hdl.handle.net/10027/19463 

Automated traffic light recognition is a key technology of interest in applications involving autonomous vehicles and safe driving. There are other important applications such asassistive technology for visually impaired pedestrians w he re traffic recognition is relevant. Crossing streets and navigating in crowded environments like cities can b e very hard for the significant segment of population that is blind or visually impaired. In the past few years, several research efforts were undertaken and papers published on regular traffic light detection. Some attempts have been made to detect traffic light signals but a comprehensive method still has to b e develop ed. The main goal of the work in this thesis was to develop technology to b e integrated in a simple wearable device for blind people to help them navigate outdoors to perform their everyday life activities and in particular to cross streets safely. The problem of automated traffic light in the absence of infrastructure is addressed. The proposed method examined various alternatives and an algorithm was devised to detect traffic lights by first selecting possible candidates by performing traffic light color extraction, pruning the large candidate set using traffic light properties, next carrying out recognition and classification of lights before finally making a decision on the traffic light signal. When tested on a set of image data, the algorithm achieved go o d results with the estimated correct detection rate of the prototype determined to b e above 90 %. The detection of pedestrian traffic signals indicating “walk and don't walk" was also considered and the algorithm devised for this problem also yielde d go o d results, again with an estimated correct detection rate better than 90 % Advisors/Committee Members: Ansari, Rashid (advisor), Radakovic, Daniela (committee member), Passerone, Claudio (committee member).

Subjects/Keywords: traffic light recognition; blind aid; machine learning

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Manavella, A. (2015). Traffic Light Detection for Portable Assistive Device to Aid Blind Pedestrians. (Thesis). University of Illinois – Chicago. Retrieved from http://hdl.handle.net/10027/19463

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Manavella, Andrea. “Traffic Light Detection for Portable Assistive Device to Aid Blind Pedestrians.” 2015. Thesis, University of Illinois – Chicago. Accessed March 07, 2021. http://hdl.handle.net/10027/19463.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Manavella, Andrea. “Traffic Light Detection for Portable Assistive Device to Aid Blind Pedestrians.” 2015. Web. 07 Mar 2021.

Vancouver:

Manavella A. Traffic Light Detection for Portable Assistive Device to Aid Blind Pedestrians. [Internet] [Thesis]. University of Illinois – Chicago; 2015. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/10027/19463.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Manavella A. Traffic Light Detection for Portable Assistive Device to Aid Blind Pedestrians. [Thesis]. University of Illinois – Chicago; 2015. Available from: http://hdl.handle.net/10027/19463

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

2. WEI, KEQI. MULTIPLE TRAFFIC LIGHT RECOGNITION SYSTEM BASED ON A MONOCULAR CAMERA.

Degree: MASc, 2017, McMaster University

URL: http://hdl.handle.net/11375/23081 

This thesis proposes a novel multiple traffic light recognition system based on videos captured by a monocular camera. Advanced driver assistance system (ADAS) and autonomous driving system (ADS) are becoming increasingly important to help drivers maneuvering vehicles and increase the vehicle and road safety in modern life. Traffic light recognition system is a significant part of ADAS and ADS, which can detect traffic light on the road and recognize different types of traffic lights to provide useful signal information for drivers. The proposed method can be applied to real complex environment only based on a monocular camera and is tested in real-world scenarios. This system consists of three parts: multiple traffic light detection, multi-target tracking and state classification. For the first step, a supervised machine learning method, support vector machine (SVM) with two integral features - histogram of oriented gradients (HOG) and histogram of CIELAB color space (HCIELAB), are used to detect traffic lights in the captured image. Then, a new multi-target tracking algorithm is presented to improve the accuracy of detection, reduce the number of false alarm and missing targets, by means of nearest neighbor data association, motion model analysis and Lucas-Kanade optical flow tracking and the region of interest (ROI) prediction. Finally, a SVM-based and a convolution neural network (CNN) based classifiers are introduced to classify the state of traffic lights, that provides the stop, go, warning, straight and turn information. Various experiments have been conducted to demonstrate the practicability of the proposed method. Both GPU-based and CPU-based programming can run real-time on the real street environment.

Thesis

Master of Applied Science (MASc)

Advisors/Committee Members: Kirubarajan, Thia, Electrical and Computer Engineering.

Subjects/Keywords: multiple traffic light recognition

…Traffic light TLR - Traffic light recognition viii Contents Abstract iv… …INTRODUCTION 1.1 Multiple Traffic Light Recognition System Traffic light recognition (TLR)… …methods have been widely used to deal with multiple traffic light recognition (MTLR)… …In this thesis, a novel multiple traffic light recognition system based on videos captured… …Lucas-Kanade MTLR - Multiple traffic lights recognition NN - Nearest neighbor RNN… 

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

WEI, K. (2017). MULTIPLE TRAFFIC LIGHT RECOGNITION SYSTEM BASED ON A MONOCULAR CAMERA. (Masters Thesis). McMaster University. Retrieved from http://hdl.handle.net/11375/23081

Chicago Manual of Style (16th Edition):

WEI, KEQI. “MULTIPLE TRAFFIC LIGHT RECOGNITION SYSTEM BASED ON A MONOCULAR CAMERA.” 2017. Masters Thesis, McMaster University. Accessed March 07, 2021. http://hdl.handle.net/11375/23081.

MLA Handbook (7th Edition):

WEI, KEQI. “MULTIPLE TRAFFIC LIGHT RECOGNITION SYSTEM BASED ON A MONOCULAR CAMERA.” 2017. Web. 07 Mar 2021.

Vancouver:

WEI K. MULTIPLE TRAFFIC LIGHT RECOGNITION SYSTEM BASED ON A MONOCULAR CAMERA. [Internet] [Masters thesis]. McMaster University; 2017. [cited 2021 Mar 07]. Available from: http://hdl.handle.net/11375/23081.

Council of Science Editors:

WEI K. MULTIPLE TRAFFIC LIGHT RECOGNITION SYSTEM BASED ON A MONOCULAR CAMERA. [Masters Thesis]. McMaster University; 2017. Available from: http://hdl.handle.net/11375/23081


Delft University of Technology

3. Bos, Evert (author). Including traffic light recognition in general object detection with YOLOv2.

Degree: 2019, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:09f32632-04eb-4907-9100-766590dc2d03 

With an in vehicle camera many different things can be done that are essential for ADAS or autonomous driving mode in a vehicle. First, it can be used for detection of general objects, for example cars, cyclists or pedestrians. Secondly, the camera can be used for traffic light recognition, which is localization of traffic light position and traffic light state recognition. No method exists at the moment able to perform general object detection and traffic light recognition at the same time, therefore this work proposes methods to combine general object detection and traffic light recognition. The novel method presented is including traffic light recognition in a general object detection framework. The single shot object detector YOLOv2 is used as base detector. As general object class dataset COCO is used and the traffic light dataset is LISA. Two different methods for combined detection are proposed: adaptive combined training and YOLOv2++. For combined training YOLOv2 is trained on both datasets with the YOLOv2 network unchanged and the loss function adapted to optimize training on both datasets. For YOLOv2++ the feature extractor of YOLOv2 pre-trained on COCO is used as feature extractor. On the features LISA traffic light states are trained with a small sub-network. It is concluded the best performing method is adaptive combined training which reaches for IOU 0.5 a AUC of 24.02% for binary and 21.23% for multi-class classification. For IOU of 0.1 this increases to 56.74% for binary and 41.87% for multi-class classification. The performance of the adaptive combined detector is 20% lower than the baseline performance of an detector only detecting LISA traffic light states and 5% lower than the baseline of a detector only detecting COCO classes, however detection of classes from both dataset is almost twice as fast as separate detection with different networks for both datasets.

mech

Advisors/Committee Members: Kooij, Julian (mentor), Pool, Ewoud (graduation committee), Gavrila, Dariu (graduation committee), Kober, Jens (graduation committee), Delft University of Technology (degree granting institution).

Subjects/Keywords: Traffic Light recognition; machine learning; YOLO; object detection; COCO; LISA

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Bos, E. (. (2019). Including traffic light recognition in general object detection with YOLOv2. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:09f32632-04eb-4907-9100-766590dc2d03

Chicago Manual of Style (16th Edition):

Bos, Evert (author). “Including traffic light recognition in general object detection with YOLOv2.” 2019. Masters Thesis, Delft University of Technology. Accessed March 07, 2021. http://resolver.tudelft.nl/uuid:09f32632-04eb-4907-9100-766590dc2d03.

MLA Handbook (7th Edition):

Bos, Evert (author). “Including traffic light recognition in general object detection with YOLOv2.” 2019. Web. 07 Mar 2021.

Vancouver:

Bos E(. Including traffic light recognition in general object detection with YOLOv2. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Mar 07]. Available from: http://resolver.tudelft.nl/uuid:09f32632-04eb-4907-9100-766590dc2d03.

Council of Science Editors:

Bos E(. Including traffic light recognition in general object detection with YOLOv2. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:09f32632-04eb-4907-9100-766590dc2d03

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