Traffic Light Detection Based on Multi-feature Segmentation and Online Selecting Scheme

Zong, Wenhao; Chen, Qijun

doi:10.1109/smc.2014.6973908

Cited by 8 publications

(4 citation statements)

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“…A CIELab color model is used in research works [20], [21] and [22]. Color spaces YCbCr, YUV and HCL are also presented in research works [23], [24] and [25] respectively.…”

Section: State Of the Artmentioning

confidence: 99%

Traffic Light Detection: A cost effective approach

Iftikhar,

Riaz,

Ali

et al. 2021

VFAST trans. softw. eng.

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In last couple of decades, the technological advancements in image and video processing has brought great revolution in our life. Some of the key areas where these advancements have played a key role are: autonomous vehicles, drone technology, crowd monitoring, traffic monitoring, object tracking etc. Nowadays a lot of work is under process for improving capabilities of autonomous vehicles and driver assisted systems. Our focus in this paper is related to automated traffic light detection system with improved capabilities in terms of time complexity and accuracy. The time complexity is directly related to image or video quality with regard to resolution of video and the accuracy is often compromised because of identification of similar objects. The similar objects often appear in video frames when each frame of video is analyzed completely. In order to solve the problem of real time detection of traffic lights in a high-resolution video having 30 frames per second with a resolution of 1280 × 720, we propose an algorithm that systematically searches in middle 70% region of each frame. The proposed algorithm optimizes the search space by dividing middle region into 3. There are three methods for searching and registering a traffic light is proposed in this paper. The basic concept is at single instance a traffic light can exist on one of these three regions. These trategies help in reducing computation complexity tremendously. The Hough Circle Transform technique of image processing is exploited to accurately detect red and green circles of light in the traffic light. Efficacy of the proposed technique in terms of improved time and accuracy is demonstrated on a real dataset collected from Nexar (dashcam mobile solution provider), it encompasses different illumination conditions: day, evening, night, cludy weather and rain etc.

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“…A CIELab color model is used in research works [20], [21] and [22]. Color spaces YCbCr, YUV and HCL are also presented in research works [23], [24] and [25] respectively.…”

Section: State Of the Artmentioning

confidence: 99%

Traffic Light Detection: A cost effective approach

Iftikhar,

Riaz,

Ali

et al. 2021

VFAST trans. softw. eng.

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“…Other less common techniques have also been noticed such as Adaptive Filters [21], Template Matching [22], Gaussian Distribution [23], Estimation of probability class associated with CNN [4] and Top Hat [24]. The use of image processing algorithms as one of the main techniques is also common; color or shape segmentation, for instance, is considered by [25], [26], [27] and [28].…”

Section: Current Approaches For Smart Tlr Devicementioning

confidence: 99%

Frequency Maps as Expert Instructions to Lessen Data Dependency on Real-time Traffic Light Recognition

Macedo¹,

Almeida²,

Matos³

et al. 2020

Preprint

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Research on Traffic Light Recognition (TLR) has grown in recent years, primarily driven by the growing interest in autonomous vehicles development. Machine Learning algorithms have been widely used to that purpose. Mainstream approaches, however, require large amount of data to properly work, and as a consequence, a lot of computational resources. In this paper we propose the use of Expert Instruction (IE) as a mechanism to reduce the amount of data required to provide accurate ML models for TLR. Given an image of the exterior scene taken from the inside of the vehicle, we stand the hypothesis that the picture of a traffic light is more likely to appear in the central and upper regions of the image. Frequency Maps of traffic light location were thus constructed to confirm this hypothesis. The frequency maps are the result of a manual effort of human experts in annotating each image with the coordinates of the region where the traffic light appears. Results show that EI increased the accuracy obtained by the classification algorithm in two different image datasets by at least 15%. Evaluation rates achieved by the inclusion of EI were also higher in further experiments, including traffic light detection followed by classification by the trained algorithm. The inclusion of EI in the PCANet achieved a precision of 83% and recall of 73% against 75.3% and 51.1%, respectively, of its counterpart. We finally presents a prototype of a TLR Device with that expert model embedded to assist drivers. The TLR uses a smartphone as a camera and processing unit. To show the feasibility of the apparatus, a dataset was obtained in real time usage and tested in an Adaptive Background Suppression Filter (AdaBSF) and Support Vector Machines (SVMs) algorithm to detect and recognize traffic lights. Results show precision of 100% and recall of 65%.

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“…Some less common techniques used alone or in association with the ones cited before are Adaptive Filters [2], Template Matching [20], Gaussian Distribution [21], Probability Estimation with CNN [3], and Top Hat [22]. Processing image algorithms are also commonly used to detect traffic lights: color or shape segmentation was used by [23,24] whereas threshold was used by [25,26].…”

Section: Related Workmentioning

confidence: 99%

“…The lowest precision rate was achieved by Template Matching, while all the other approaches have obtained above an 80% precision rate, including Template Matching in other tests in the same paper where the worst result was accounted. Color or Shape Segmentation/HOG/SVM --89.90 [30] Color or Shape Segmentation/SVM 86.20 95.50 - [21] Gaussian Distribution --80.00-85.00 [16] Geometric Transforms --56.00-93.00 [34] Color or Shape Segmentation/Histograms --97.50 [7] PCAnet/SVM --97.50 [6] CNN/Saliency Map --96.25 [24] Color or Shape Segmentation --92.00-96.00 [19] Geometric Transforms 87.32 84.93 - [17] Geometric Transforms --70.00 [25] Color or Shape Segmentation/Threshold --88.00-96.00 [35] Color or Shape Segmentation/Histograms --50.00-83.33 [32] Color or Shape Segmentation/SVM 98.96 99.18 - [20] Template Matching 98.00 97.00 - [43] Hidden Markov Models --90.55 [38] Template Matching --90.50 [22] Top Hat --97.00 [39] Template Matching --69.23 [36] Histograms --91.00 [41] Probability Histograms --94.00 [18] Geometric Transforms/Histograms --89.00 [40] Template Matching 98.41 95.38 - [44] Template Matching 44.00-63.00 75.00-94.00 -Data used in the related works are not always made available by the authors, and, when available, only a few are complete, i.e., contains separate traffic light images and whole traffic scene images. In Table 2, the Type column refers to what kind of traffic light the dataset contains, the Traffic light samples column shows how many images containing only a traffic light exists, these images are very useful to train Machine Learning algorithms and are obtained from whole frames containing traffic scenes.…”

Section: Related Workmentioning

confidence: 99%

Prototyping a Traffic Light Recognition Device with Expert Knowledge

et al. 2018

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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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Traffic Light Detection Based on Multi-feature Segmentation and Online Selecting Scheme

Cited by 8 publications

References 8 publications

Traffic Light Detection: A cost effective approach

Traffic Light Detection: A cost effective approach

Frequency Maps as Expert Instructions to Lessen Data Dependency on Real-time Traffic Light Recognition

Prototyping a Traffic Light Recognition Device with Expert Knowledge

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