VSSA-NET: Vertical Spatial Sequence Attention Network for Traffic Sign Detection

Although traffic sign detection has been studied for years and great progress has been made with the rise of deep learning technique, there are still many problems remaining to be addressed. For complicated real-world traffic scenes, there are two main challenges. Firstly, traffic signs are usually small size objects, which makes it more difficult to detect than large ones; Secondly, it is hard to distinguish false targets which resemble real traffic signs in complex street scenes without context information. To handle these problems, we propose a novel end-to-end deep learning method for traffic sign detection in complex environments. Our contributions are as follows: 1) We propose a multi-resolution feature fusion network architecture which exploits densely connected deconvolution layers with skip connections, and can learn more effective features for the small size object; 2) We frame the traffic sign detection as a spatial sequence classification and regression task, and propose a vertical spatial sequence attention (VSSA) module to gain more context information for better detection performance. To comprehensively evaluate the proposed method, we do experiments on several traffic sign datasets as well as the general object detection dataset and the results have shown the effectiveness of our proposed method

Effortless Deep Training for Traffic Sign Detection Using Templates and Arbitrary Natural Images

Deep learning has been successfully applied to several problems related to autonomous driving. Often, these solutions rely on large networks that require databases of real image samples of the problem (i.e., real world) for proper training. The acquisition of such real-world data sets is not always possible in the autonomous driving context, and sometimes their annotation is not feasible (e.g., takes too long or is too expensive). Moreover, in many tasks, there is an intrinsic data imbalance that most learning-based methods struggle to cope with. It turns out that traffic sign detection is a problem in which these three issues are seen altogether. In this work, we propose a novel database generation method that requires only (i) arbitrary natural images, i.e., requires no real image from the domain of interest, and (ii) templates of the traffic signs, i.e., templates synthetically created to illustrate the appearance of the category of a traffic sign. The effortlessly generated training database is shown to be effective for the training of a deep detector (such as Faster R-CNN) on German traffic signs, achieving 95.66% of mAP on average. In addition, the proposed method is able to detect traffic signs with an average precision, recall and F1-score of about 94%, 91% and 93%, respectively. The experiments surprisingly show that detectors can be trained with simple data generation methods and without problem domain data for the background, which is in the opposite direction of the common sense for deep learning

Automatic Traffic Sign Detection and Recognition Using Colour Segmentation and Shape Identification

The paper describes a colour-based segmentation method of European traffic signs for detection in an image and a feature-based recognition method for categorizing them into given classes. At first, we have performed analysis of several well-known colour spaces as the RGB, HSV and YCbCr often used for segmentation purposes. The HSV colour space has been chosen as the most convenient for segmentation step and colour-based models of traffic signs representatives were created. Next, the fast radial symmetry (FRS) detection method and the Harris corner detector were used to recognize circles, triangles and squares as main geometrical shapes of the traffic signs. For these purposes a new gallery of real-life images containing traffic signs has been created and analysed. Overall efficiency of our recognition method is approx. 93 % on our gallery and is usable for real-time implementations

Advisory speed for Intelligent Speed Adaptation in adverse conditions

In this paper, a novel approach to compute advisory speeds to be used in an adaptive Intelligent Speed Adaptation system (ISA) is proposed. This method is designed to be embedded in the vehicles. It estimates an appropriate speed by fusing in real-time the outputs of ego sensors which detect adverse conditions with roadway characteristics transmitted by distant servers. The method presents two major novelties. First, the 85 th percentile of observed speeds (V 85 ) is estimated along a road, this speed profile is considered as a reference speed practised and practicable in ideal conditions for a lonely vehicle. In adverse conditions, this reference speed is modulated in order to account for lowered friction and lowered visibility distance (top-down approach). Second, this method allows us taking into account the potential seriousness of crashes using a generic scenario of accident. Within this scenario, the difference in speed that should be applied in adverse conditions is estimated so that global injury risk is the same as in ideal conditions

Multi-ROI Association and Tracking With Belief Functions: Application to Traffic Sign Recognition

This paper presents an object tracking algorithm using belief functions applied to vision-based traffic sign recognition systems. This algorithm tracks detected sign candidates over time in order to reduce false positives due to data fusion formalization. In the first stage, regions of interest (ROIs) are detected and combined using the transferable belief model semantics. In the second stage, the local pignistic probability algorithm generates the associations maximizing the belief of each pairing between detected ROIs and ROIs tracked by multiple Kalman filters. Finally, the tracks are analyzed to detect false positives. Due to a feedback loop between the multi-ROI tracker and the ROI detector, the solution proposed reduces false positives by up to 45%, whereas computation time remains very low

Real-Time Speed Sign Detection Using the Radial Symmetry Detector

Algorithms for classifying road signs have a high computational cost per pixel processed. A detection stage that has a lower computational cost can facilitate real-time processing. Various authors have used shape and color-based detectors. Shape-based detectors have an advantage under variable lighting conditions and sign deterioration that, although the apparent color may change, the shape is preserved. In this paper, we present the radial symmetry detector for detecting speed signs. We evaluate the detector itself in a system that is mounted within a road vehicle. We also evaluate its performance that is integrated with classification over a series of sequences from roads around Canberra and demonstrate it while running online in our road vehicle. We show that it can detect signs with high reliability in real time. We examine the internal parameters of the algorithm to adapt it to road sign detection. We demonstrate the stability of the system under the variation of these parameters and show computational speed gains through their tuning. The detector is demonstrated to work under a wide variety of visual conditions

The design and implementation of serious games for driving and mobility

The automotive and transportation sectors are showing consistent improvements in trends and standards concerning the safe and convenient travel of the road users. In this growing community of road users, the driver performance is a notable factor as many on-road mishaps emerge out of poor driver performance. In this research work, a case-study and experimental analysis were conducted to improve driver performance through the deployment of serious games. The primary motive of this work is to stimulate the on-road user performance through immediate feedback, driver coaching, and real-time gamification methods. The games exploit the cloud-based architecture to retrieve the driver performance scores based on real-time evaluation of vehicle signals and display the outcomes on game scene by reflecting the game parameters based on real-world user performance (in the context of driving and mobility). The deployment of games in cars is the topic of interest in current state-of-the-art, as there are more factors associated with it, such as safety, usability, and willingness of the users. These aspects were taken into careful consideration while designing the paradigm of gamification model. The user feedback for the real-time games was extracted through pilot tests and field tests in Genova. The gamification and driver coaching aspects were tested on various occasions (plug-in and field tests conducted at 5 European test sites), and the inputs from these field tests enabled to tune the parameters concerning the evaluation and gamification models. The improvement of user behavior was performed through a virtuous cycle with the integration of virtual sensors to the serious gaming framework. As the culmination, the usability tests for the real-time games were conducted with 18 test users to understand the user acceptance criteria and the parameters (ease of use and safety) that would contribute to the deployment of games. Other salient factors such as the impact of games, large-scale deployment, collaborative gaming and exploitation of gaming framework for 3rd party applications were also investigated in this research activity. The analysis of the usability tests states that the user acceptance of the implemented games is good. The report from usability study has addressed the user preferences in games such as duration, strategy and gameplay mechanism; these factors contribute a foundation for future research in implementing the games for mobility

Unconstrained Road Sign Recognition

There are many types of road signs, each of which carries a different meaning and function: some signs regulate traffic, others indicate the state of the road or guide and warn drivers and pedestrians. Existent image-based road sign recognition systems work well under ideal conditions, but experience problems when the lighting conditions are poor or the signs are partially occluded. The aim of this research is to propose techniques to recognize road signs in a real outdoor environment, especially to deal with poor lighting and partially occluded road signs. To achieve this, hybrid segmentation and classification algorithms are proposed. In the first part of the thesis, we propose a hybrid dynamic threshold colour segmentation algorithm based on histogram analysis. A dynamic threshold is very important in road sign segmentation, since road sign colours may change throughout the day due to environmental conditions. In the second part, we propose a geometrical shape symmetry detection and reconstruction algorithm to detect and reconstruct the shape of the sign when it is partially occluded. This algorithm is robust to scale changes and rotations. The last part of this thesis deals with feature extraction and classification. We propose a hybrid feature vector based on histograms of oriented gradients, local binary patterns, and the scale-invariant feature transform. This vector is fed into a classifier that combines a Support Vector Machine (SVM) using a Random Forest and a hybrid SVM k-Nearest Neighbours (kNN) classifier. The overall method proposed in this thesis shows a high accuracy rate of 99.4% in ideal conditions, 98.6% in noisy and fading conditions, 98.4% in poor lighting conditions, and 92.5% for partially occluded road signs on the GRAMUAH traffic signs dataset

Modellbasierter Ansatz zur probabilistischen Interpretation von Fahrsituationen

Diese Arbeit stellt ein methodisches Konzept vor, welches eine probabilistische Interpretation von Fahrsituationen auf Basis der Objekte im Fahrzeugumfeld ermöglicht. Wissen über Fahrsituationen wird in einer formalen Repräsentation abgelegt. Abhängig von den gemessenen Objektinformationen der Umfeldsensorik wird daraus ein graphisches Modell erzeugt. Aus diesem werden durch Inferenz Schlüsse über die Fahrsituation gezogen. Das Konzept wurde in Experimenten und realen Situationen erprobt

Kontextsensitive Erkennung und Interpretation fahrrelevanter statischer Verkehrselemente

In dieser Arbeit werden Methoden und Verfahren zur Umwelterkennung und Situationsinterpretation entwickelt, mit denen statische Verkehrselemente (Verkehrszeichen und Ampeln) erkannt und im Kontext der Verkehrssituation interpretiert werden. Die Praxistauglichkeit der entwickelten Methoden und Verfahren wird durch umfangreiche Experimente demonstriert, bei denen auf die Verwendung realer Daten, kostengünstiger Sensorik und Echtzeitverarbeitung Wert gelegt wird