Detection and classification of traffic lights for automated setup of road surveillance systems

Shi, Xiaobo; Zhao, Na; Xia, Yingjie

doi:10.1007/s11042-014-2343-1

Cited by 13 publications

(6 citation statements)

References 19 publications

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“…The SVM algorithm has been widely used in previous TLDR methods [27,28,30,31]. The results suggest that SVM can be employed to recognize traffic lights.…”

Section: Recognition Methods Of Traffic Light's Shapementioning

confidence: 99%

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A Lightweight Traffic Lights Detection and Recognition Method for Mobile Platform

Wang

Han

Xiang

et al. 2023

Drones

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Traffic lights detection and recognition (TLDR) is one of the necessary abilities of multi-type intelligent mobile platforms such as drones. Although previous TLDR methods have strong robustness in their recognition results, the feasibility of deployment of these methods is limited by their large model size and high requirements of computing power. In this paper, a novel lightweight TLDR method is proposed to improve its feasibility to be deployed on mobile platforms. The proposed method is a two-stage approach. In the detection stage, a novel lightweight YOLOv5s model is constructed to locate and extract the region of interest (ROI). In the recognition stage, the HSV color space is employed along with an extended twin support vector machines (TWSVMs) model to achieve the recognition of multi-type traffic lights including the arrow shapes. The dataset, collected in naturalistic driving experiments with an instrument vehicle, is utilized to train, verify, and evaluate the proposed method. The results suggest that compared with the previous YOLOv5s-based TLDR methods, the model size of the proposed lightweight TLDR method is reduced by 73.3%, and the computing power consumption of it is reduced by 79.21%. Meanwhile, the satisfied reasoning speed and recognition robustness are also achieved. The feasibility of the proposed method to be deployed on mobile platforms is verified with the Nvidia Jetson NANO platform.

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“…The SVM algorithm has been widely used in previous TLDR methods [27,28,30,31]. The results suggest that SVM can be employed to recognize traffic lights.…”

Section: Recognition Methods Of Traffic Light's Shapementioning

confidence: 99%

“…First Stage: Detection Second Stage: Recognition [27] Visual selective attention model Support vector machines (SVMs) [28] HSV color-space-based SVM [29] HSV color space with maximally stable extremal region CNN [30] RGB color-space-based SVM [31] HSV color space with Otsu algorithm SVM [32] YOLOv3…”

Section: Referencesmentioning

confidence: 99%

A Lightweight Traffic Lights Detection and Recognition Method for Mobile Platform

Wang

Han

Xiang

et al. 2023

Drones

View full text Add to dashboard Cite

show abstract

“…In the recognition phase, most work employs machine learning algorithms such as Neural Networks or Support Vector Machines (SVM). [21], [14], [29], [30], [15], [8], [13], [31], [32], [33] employ SVMs as the main technique to recognize the semaphore. A non machine learning approach to recognition can be seen in the works of [11] and [34], where Fuzzy Logic has been successfully applied.…”

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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“…In [27], the authors used a CNN whereas in [3,14] the authors used a PCAnetwork, an NN that simulates a CNN using less layers. SVMs were used by [2,7,[12][13][14][28][29][30][31][32] to recognize traffic lights, sometimes along with a NN. Fuzzy systems were also used in [10,33].…”

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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Detection and classification of traffic lights for automated setup of road surveillance systems

Cited by 13 publications

References 19 publications

A Lightweight Traffic Lights Detection and Recognition Method for Mobile Platform

A Lightweight Traffic Lights Detection and Recognition Method for Mobile Platform

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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