Currently, the visually impaired rely on either a sighted human, guide dog, or white cane to safely navigate. However, the training of guide dogs is extremely expensive, and canes cannot provide essential information regarding the color of traffic lights and direction of crosswalks. In this paper, we propose a deep learning based solution that provides information regarding the traffic light mode and the position of the zebra crossing. Previous solutions that utilize machine learning only provide one piece of information and are mostly binary: only detecting red or green lights. The proposed convolutional neural network, LYTNet, is designed for comprehensiveness, accuracy, and computational efficiency. LYTNet delivers both of the two most important pieces of information for the visually impaired to cross the road. We provide five classes of pedestrian traffic lights rather than the commonly seen three or four, and a direction vector representing the midline of the zebra crossing that is converted from the 2D image plane to real-world positions. We created our own dataset of pedestrian traffic lights containing over 5000 photos taken at hundreds of intersections in Shanghai. The experiments carried out achieve a classification accuracy of 94 , average angle error of (6.35^ ), with a frame rate of 20 frames per second when testing the network on an iPhone 7 with additional post-processing steps.
The task of detecting traffic light for autonomous driving has been explored by many and has developed over the years @cite_2 @cite_15 @cite_1 @cite_7 . @cite_8 created a model that is able to detect traffic lights as small as @math pixels and with relatively high accuracy. Though most models for traffic lights have a high precision and recall rate of nearly 100 @cite_13 were one of the first to develop an algorithm to detect pedestrian traffic lights and the length of the zebra-crossing. Others such as and @cite_14 @cite_16 both developed an analytic image processing algorithm, which undergoes candidate extraction, candidate recognition, and candidate classification. @cite_3 proposed a more robust real-time pedestrian traffic lights detection algorithm, which gets rid of the analytic image processing method and uses candidate extraction and a concise machine learning scheme.
@cite_14 @cite_7 @cite_8 @cite_1 @cite_3 @cite_2 @cite_15 @cite_16 @cite_13
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TL-recognizer detects traffic lights from a mobile device camera.Robust method for unsupervised image acquisition and segmentation.Robust solution: traffic lights are clearly visible in different light conditions.Solution is reliable: precision 1 and recall 0.8 in different light conditions.Solution is efficient: computation time 100źms on a Nexus 5. Independent mobility involves a number of challenges for people with visual impairment or blindness. In particular, in many countries the majority of traffic lights are still not equipped with acoustic signals. Recognizing traffic lights through the analysis of images acquired by a mobile device camera is a viable solution already experimented in scientific literature. However, there is a major issue: the recognition techniques should be robust under different illumination conditions.This contribution addresses the above problem with an effective solution: besides image processing and recognition, it proposes a robust setup for image capture that makes it possible to acquire clearly visible traffic light images regardless of daylight variability due to time and weather. The proposed recognition technique that adopts this approach is reliable (full precision and high recall), robust (works in different illumination conditions) and efficient (it can run several times a second on commercial smartphones). The experimental evaluation conducted with visual impaired subjects shows that the technique is also practical in supporting road crossing. The traffic sign detection and recognition system is an essential module of the driver warning and assistance system. Smart vehicles are the order of the day. Such vehicles have the ability to warn drivers of pending situations, remind them of speed limits and even automatically take evasive action. Due to the visual nature of existing infrastructure, signs and line markings, image processing will play a large part in these systems. In this paper we present an automated Traffic sign recognition system allowing an invariance localization to changes in position, scale, rotation, weather conditions, partial occlusion, and the presence of other objects of the same color. The reliability demonstrated by the proposed method suggests that this system could be a part of an integrated driver warning and assistance system based on computer vision technology. Reliable traffic light detection and classification is crucial for automated driving in urban environments. Currently, there are no systems that can reliably perceive traffic lights in real-time, without map-based information, and in sufficient distances needed for smooth urban driving. We propose a complete system consisting of a traffic light detector, tracker, and classifier based on deep learning, stereo vision, and vehicle odometry which perceives traffic lights in real-time. Within the scope of this work, we present three major contributions. The first is an accurately labeled traffic light dataset of 5000 images for training and a video sequence of 8334 frames for evaluation. The dataset is published as the Bosch Small Traffic Lights Dataset and uses our results as baseline. It is currently the largest publicly available labeled traffic light dataset and includes labels down to the size of only 1 pixel in width. The second contribution is a traffic light detector which runs at 10 frames per second on 1280×720 images. When selecting the confidence threshold that yields equal error rate, we are able to detect traffic lights as small as 4 pixels in width. The third contribution is a traffic light tracker which uses stereo vision and vehicle odometry to compute the motion estimate of traffic lights and a neural network to correct the aforementioned motion estimate. The recognition and tracking of traffic lights for intelligent vehicles based on a vehicle-mounted camera are studied in this paper. The candidate region of the traffic light is extracted using the threshold segmentation method and the morphological operation. Then, the recognition algorithm of the traffic light based on machine learning is employed. To avoid false negatives and tracking loss, the target tracking algorithm CAMSHIFT (Continuously Adaptive Mean Shift), which uses the color histogram as the target model, is adopted. In addition to traffic signal pre-processing and the recognition method of learning, the initialization problem of the search window of CAMSHIFT algorithm is resolved. Moreover, the window setting method is used to shorten the processing time of the global HSV color space conversion. The real vehicle experiments validate the performance of the presented approach. In defect of intelligent assistant approaches, the visually impaired feel hard to cross the roads in urban environments. Aiming to tackle the problem, a real-time Pedestrian Crossing Lights (PCL) detection algorithm for the visually impaired is proposed in this paper. Different from previous works which utilize analytic image processing to detect the PCL in ideal scenarios, the proposed algorithm detects PCL using machine learning scheme in the challenging scenarios, where PCL have arbitrary sizes and locations in acquired image and suffer from the shake and movement of camera. In order to achieve the robustness and efficiency in those scenarios, the detection algorithm is designed to include three procedures: candidate extraction, candidate recognition and temporal-spatial analysis. A public dataset of PCL, which includes manually labeled ground truth data, is established for tuning parameters, training samples and evaluating the performance. The algorithm is implemented on a portable PC with color camera. The experiments carried out in various practical scenarios prove that the precision and recall of detection are both close to 100 , meanwhile the frame rate is up to 21 frames per second (FPS). Traffic lights detection and recognition research has grown every year. Time is coming when autonomous vehicle can navigate in urban roads and streets and intelligent systems aboard those cars would have to recognize traffic lights in real time. This article proposes a traffic light recognition (TLR) device prototype using a smartphone as camera and processing unit that can be used as a driver assistance. A TLR device has to be able to visualize the traffic scene from inside of a vehicle, generate stable images, and be protected from adverse conditions. To validate this layout prototype, a dataset was built and used to test an algorithm that uses an adaptive background suppression filter (AdaBSF) and Support Vector Machines (SVMs) to detect traffic lights. The application of AdaBSF and subsequent classification with SVM to the dataset achieved 100 precision rate and recall of 65 . Road testing shows that the TLR device prototype meets the requirements to be used as a driver assistance device. The concern of the intelligent transportation system rises and many driver support systems have been developed. In this paper, a fast method of detecting a traffic light in a scene image is proposed. By converting the color space from RGB to normalized RGB, some regions are selected as candidates of a traffic light. Then a method based on the Hough transform is applied to detect an exact region. Experimental results using images including a traffic light verifies the effectiveness of the proposed method. In this paper we introduce a real-time traffic light recognition system for intelligent vehicles. The method proposed is fully based on image processing. Detection step is achieved in grayscale with spot light detection, and recognition is done using our generic “adaptive templates”. The whole process was kept modular which make our TLR capable of recognizing different traffic lights from various countries. This paper proposes a method for measurement of the length of a pedestrian crossing and for the detection of traffic lights from image data observed with a single camera. The length of a crossing is measured from image data of white lines painted on the road at a crossing by using projective geometry. Furthermore, the state of the traffic lights, green (go signal) or red (stop signal), is detected by extracting candidates for the traffic light region with colour similarity and selecting a true traffic light from them using affine moment invariants. From the experimental results, the length of a crossing is measured with an accuracy such that the maximum relative error of measured length is less than 5 and the rms error is 0.38 m. A traffic light is efficiently detected by selecting a true traffic light region with an affine moment invariant.