Traffic lights detection and state estimation using Hidden Markov Models

Gómez, Andrés E.; Alencar, Francisco A.R.; Prado, Paulo Victor de Souza; Osório, Fernando Santos; Wolf, Denis F.

doi:10.1109/ivs.2014.6856486

Cited by 44 publications

(29 citation statements)

References 12 publications

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“…Normalized Cross Correlation was observed in [42] to recognize pedestrian traffic lights. Hidden Markov Models (HMM) were used in [43] to recognize common traffic lights. Table 1 relates the rates obtained by the works previously mentioned and its main techniques.…”

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%

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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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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Prototyping a Traffic Light Recognition Device with Expert Knowledge

et al. 2018

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“…Aimed at specific urban traffic conditions, more precise evaluation models are built by least absolute shrinkage and selection operator (LASSO) [4], Neural Network [5], hidden markov model (HMM) [6], support vector machine (SVM) [7], etc. However, these approaches have the following deficiencies when applied to the traffic congestion evaluation system based on floating car data.…”

Section: Smooth Average Congestedmentioning

confidence: 99%

“…One is to generate assessment models directly by analyzing a large number of unlabeled traffic data [1][2][3][4]. The alternative approach is judged by the citizens or traffic governors [5,6]. They make a subjective assessment of congestion and produce some labeled samples, based on the observation of road conditions.…”

Section: Introductionmentioning

confidence: 99%

Urban Traffic Congestion Evaluation Based on Kernel the Semi-Supervised Extreme Learning Machine

2017

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Abstract:There is always an asymmetric phenomenon between traffic data quantity and unit information content. Labeled data is more effective but scarce, while unlabeled data is large but weaker in sample information. In an urban transportation assessment system, semi-supervised extreme learning machine (SSELM) can unite manual observed data and extensively collected data cooperatively to build connection between congestion condition and road information. In our method, semi-supervised learning can integrate both small-scale labeled data and large-scale unlabeled data, so that they can play their respective advantages, while the ELM can process large scale data at high speed. Optimized by kernel function, Kernel-SSELM can achieve higher classification accuracy and robustness than original SSELM. Both the experiment and the real-time application show that the evaluation system can precisely reflect the traffic condition.

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“…To enable TLR, self-driving vehicles are usually equipped with one or more forward-looking cameras that capture traffic scenes from the driver point of view. The images are later processed in order to detect (i.e., locate) traffic lights based on structural and/or appearance models [Gómez et al 2014, Diaz-Cabrera et al 2015, Li et al 2018], or using learning-based techniques [Lindner et al 2004, Franke et al 2013, Barnes et al 2015.…”

Section: Introductionmentioning

confidence: 99%

Relevant Traffic Light Localization via Deep Regression

Freitas

Paixão

Berriel

et al. 2019

Anais Do XVI Encontro Nacional De Inteligência Artificial E Computacional (ENIAC 2019)

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Artificial intelligence advances have an important role on self-driving cars development, such as assisting the recognition of traffic lights. However, when relying on images of the scene alone, little progress was observed on selecting the traffic lights defining guidance to the car. Common detection approaches rely on additional high-level decision-making process to select a relevant traffic light. This work address the problem by proposing a deep regression system with an outliers resilient loss to predict the coordinates of a relevant traffic light in the image plane. The prediction can be used as a high-level decision-maker or as an assistant to a cheaper classifier to work on a region of interest. Results for European scenes show success in about 88% of the cases.

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Traffic lights detection and state estimation using Hidden Markov Models

Cited by 44 publications

References 12 publications

Prototyping a Traffic Light Recognition Device with Expert Knowledge

Prototyping a Traffic Light Recognition Device with Expert Knowledge

Urban Traffic Congestion Evaluation Based on Kernel the Semi-Supervised Extreme Learning Machine

Relevant Traffic Light Localization via Deep Regression

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