An efficient vision-based traffic light detection and state recognition for autonomous vehicles

Saini, Sanjay; Nikhil, S; Konda, Krishna Reddy; Bharadwaj, Harish S; Ganeshan, N

doi:10.1109/ivs.2017.7995785

Cited by 71 publications

(34 citation statements)

References 13 publications

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“…Color space Verification / Classification [2], [3] Gray-scale Template matching [4] RGB K-means clustering, Circularity check [5] RGB Region growing, Color segmentation [6] Normalized RGB Color segmentation, Circle Hough transform [7] Ruta's RGB SVM [8] YCbCr Adaboost [9] YCbCr Decision-tree classifier [10] HSI Gaussian mask, Existence-Weight Map [11] HSV Template matching [14] CIE Lab Fast radial symmetry transform [12] HSV SVM [13] HSL SVM [15] Normalized RGB, RGB Color clustering [16] Normalized RGB, RGB Fuzzy logic clustering [17] RGB, YCbCr Nearest neighbor classifier [18] RGB, HSV LDA, kNN, SVM [53] CIE Lab SVM, LeNet, AlexNet [52] HSV SVM, Simple CNN [54] RGB YOLO v1 [55] RGB YOLO 9000…”

Section: Ref #mentioning

confidence: 99%

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An Efficient Color Space for Deep-Learning Based Traffic Light Recognition

Kim

Park

Jung

2018

Journal of Advanced Transportation

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Traffic light recognition is an essential task for an advanced driving assistance system (ADAS) as well as for autonomous vehicles. Recently, deep-learning has become increasingly popular in vision-based object recognition owing to its high performance of classification. In this study, we investigate how to design a deep-learning based high-performance traffic light detection system. Two main components of the recognition system are investigated: the color space of the input video and the network model of deep learning. We apply six color spaces (RGB, normalized RGB, Ruta’s RYG, YCbCr, HSV, and CIE Lab) and three types of network models (based on the Faster R-CNN and R-FCN models). All combinations of color spaces and network models are implemented and tested on a traffic light dataset with 1280×720 resolution. Our simulations show that the best performance is achieved with the combination of RGB color space and Faster R-CNN model. These results can provide a comprehensive guideline for designing a traffic light detection system.

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Section: Ref #mentioning

confidence: 99%

“…The abovementioned deep-learning methods have been widely applied to detect objects such as vehicle and pedestrian [47][48][49][50][51]. However, only a few deep-learning based network models have been applied to traffic light detection system [52][53][54][55].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Color Space for Deep-Learning Based Traffic Light Recognition

Kim

Park

Jung

2018

Journal of Advanced Transportation

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“…Additionally, we will focus on the remaining paths in Figure 1 and work on ASVI solutions based on ADAS algorithms for obstacle, especially bike and vehicle, detection (e. g. [13], [22], [35]) as well as traffic light (e. g. [27]) and traffic sign detection (e. g. [36]). The adaptations for all considered use cases will be summarized in a concept for transferring ADAS algorithms to ASVI using methods from software engineering [30] and project management [26].…”

Section: Future Workmentioning

confidence: 99%

Camera-based On-Road Detections for the Visually Impaired

Jakob¹,

Tick²

2020

ACTA POLYTECH HUNG

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Herein, we research the possibilities of assisting visually impaired pedestrians moving in traffic situations by using camera-based detection of relevant objects in their immediate surroundings. Therefore, we use and adapt algorithms from the field of driver assistance. We present a road background segmentation based on watersheds, whose results are used as input for the presented crosswalk and lane detection algorithms. The crosswalk detection is based on the application of two 1D mean filters and the lane detection on local computations of the EDF (Edge Distribution Function). In our evaluation, the described algorithms achieved good hit rates of 99.87% (road segmentation), 98.64% (crosswalk detection), and 97.89% (lane detection).

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“…2 Proposed procedure to adapt the Lane Detection algorithm in [12] for visually impaired pedestrians. In [26], a Traffic Light (State) Detection based on HSV color space, Maximally Stable Extremal Region (MSER), Histogram of Oriented Gradients (HOG) features, SVM, and Convolutional Neural Networks (CNN) is presented. Additionally, [26] describes the general structure of algorithms for Traffic Light (State) Detection.…”

Section: Divide Into Subregionsmentioning

confidence: 99%

“…In [26], a Traffic Light (State) Detection based on HSV color space, Maximally Stable Extremal Region (MSER), Histogram of Oriented Gradients (HOG) features, SVM, and Convolutional Neural Networks (CNN) is presented. Additionally, [26] describes the general structure of algorithms for Traffic Light (State) Detection. First, candidates for traffic lights are identified by their color for which different color spaces can be used.…”

Section: Divide Into Subregionsmentioning

confidence: 99%

Traffic Scenarios and Vision Use Cases for the Visually Impaired

Jakob¹,

Tick²

2018

AEI

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We gather the requirements visually impaired road users have concerning a camera-based assistive system in order to develop a concept for transferring algorithms from Advanced Driver Assistance Systems (ADAS) to this domain. We therefore combine procedures from software engineering, especially requirements engineering, with methods from qualitative social sciences, namely expert interviews by means of problem-centered interview methodology. The evaluation of the interviews results in a total of six traffic scenarios, that are of interest for the assistance of visually impaired road users, clustered into three categories: orientation, pedestrian and public transport scenarios. From each scenario, we derive use cases based on computer vision and state which of them are also addressed in ADAS so that we can examine them further in the future to formulate the transfer concept. We present a general literature review concerning solutions from ADAS for the identified overlapping use cases from both fields and take a closer look on how to adapt ADAS Lane Detection algorithms to the assistance of the visually impaired.

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An efficient vision-based traffic light detection and state recognition for autonomous vehicles

Cited by 71 publications

References 13 publications

An Efficient Color Space for Deep-Learning Based Traffic Light Recognition

An Efficient Color Space for Deep-Learning Based Traffic Light Recognition

Camera-based On-Road Detections for the Visually Impaired

Traffic Scenarios and Vision Use Cases for the Visually Impaired

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