A vision based system for traffic lights recognition

Salarian, Mehdi; Manavella, Andrea; Ansari, Rashid

doi:10.1109/intellisys.2015.7361224

Cited by 20 publications

(3 citation statements)

References 5 publications

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“…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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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

View full text Add to dashboard Cite

show abstract

“…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%

“…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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