Wajdi Farhat

Embedded system for road sign detection using MicroBlaze

In this paper, we present implementation of road sign detection application in an embedded real time system. The target hardware is a Xilinx MicroBlaze soft-processor. The algorithm is described using C language and compiled with the SDK and EDK tools targeting the VirtexML605 FPGA. The input video is a real scene acquired by a digital camera with resolution of 480×640 pixels. Adequate architecture generated of the MicroBlaze with adequate communication with the system memory, allows real time execution of the application. Experimental results on synthetic and real data show that our implementation successfully runs in real time with a low occupation of resources, while maintaining comparable functionality to version existing solutions based on FPGA. The proposed hardware platform is faster compared to existing solutions based DSP.

Novel approach for real time detection and classification based on template matching in video

In this paper, we present a new approach for the detection and classification of real time road signs from video. The proposed system is composed of two processing stages: detection and classification signs. The system has detected road signs by color and shape feature of segmentation color in HSV color space, especially red and blue. The detected signs are then classified by method template matching, such as circular, squared and triangular shapes. The road signs are classified in one of the following categories by Combining color and information about shape: danger, obligation, prohibition or information. As proposed system input is a video with a resolution of 1360×800 pixels. For detection phase road signs, has a high detection performance up to 92 % and for classification is rated 96% in our experiments. The proposed system also proves to be reliable and suitable for real-time processing.

Real-time embedded system for traffic sign recognition based on ZedBoard

AbstractThis paper presents a design methodology of a real-time embedded system that processes the detection and recognition of road signs while the vehicle is moving. An efficient algorithm was proposed, which operates in two processing steps: the detection and the recognition. Regions of interest were extracted by using the Maximally Stable Extremal Regions Method. For the recognition phase, Oriented FAST and Rotated BRIEF features were used. A hardware system based on the Xilinx Zynq platform was developed. The designed system can achieve real-time video processing while assuring constraints and a high-level accuracy in terms of detection and recognition rates.

Effect of color spaces on video segmentation performances

Color segmentation is a preliminary step in many application computer vision systems today, as the detection of human movement, recognition of road traffic signs and video intelligent. Detection stage performance is therefore closely linked to the results obtained from the color segmentation. Detection and recognition automatic road traffic signs are applied in the color spaces RGB, HSV, and HSI. We present in this paper a comparative study on the color detection of road signs by thresholding according to the color spaces: RGB, HSV and HSI We proceed after that to verify the results of the detection rate and false detection rate obtained for each color space. The results have been validated on video under different lighting conditions.

Real-time recognition of road traffic signs in video scenes

This paper presents a new algorithm for the detection and classification of real-time road traffic signs in the video. Our system is able to detect and classify triangular, circular, and octagonal signs of red and blue colors. The proposed system operates into two processing steps: (1) detection and (2) classification road signs. The system has detected candidate regions as Maximally Stable Extremal Regions (MSERs) in HSV color space with available robustness to variations in lighting conditions, especially red and blue. The detected candidate regions were then classified as method template matching, such as circular, octagonal, and triangular shapes. The proposed system is operating under a range of weather conditions and recognizes all classes of road traffic sign database. Results show a high success rate. In fact, the system maintains high performance for detection and classification steps whose F-measure are set to 0.95 and 0.92 respectively. We conclude, from these results, that the proposed system is invariant to rotation, translation, scale, even to partial occlusions. Moreover, results prove that the system is suitable and reliable for real-time processing.

Novel Technique for 3D Face Recognition Using Anthropometric Methodology

This manuscript presents an improved system research that can detect and recognize the person in 3D space automatically and without the interaction of the peoples faces. This system is based not only on a quantum computation and measurements to extract the vector features in the phase of characterization but also on learning algorithm using SVM to classify and recognize the person. This research presents an improved technique for automatic 3D face recognition using anthropometric proportions and measurement to detect and extract the area of interest which is unaffected by facial expression. This approach is able to treat incomplete and noisy images and reject the non-facial areas automatically. Moreover, it can deal with the presence of holes in the meshed and textured 3D image. It is also stable against small translation and rotation of the face. All the experimental tests have been done with two 3D face datasets FRAV 3D and GAVAB. Therefore, the tests results of the proposed approach are promising because they showed that it is competitive comparable to similar approaches in terms of accuracy, robustness, and flexibility. It achieves a high recognition performance rate of 95.35% for faces with neutral and non-neutral expressions for the identification and 98.36% for the authentification with GAVAB and 100% with some gallery of FRAV 3D datasets.

Architectural exploration of multilayer perceptron models for on-chip and real-time road sign classification

Road sign recognition is part of the automatic driver assistance systems implemented on the dashboard of vehicles. The recognition task is often carried out based on a classification procedure manipulating the detected signs. Classification tasks can be resolved by the use of multilayer artificial neural network systems. This article proposes an optimized real-time on-chip hardware implementation of multilayer perceptron system used for road sign classification. Four architectural approaches were described: on the one hand, the classic and the serial optimized architectures that offer a very significant reduction in hardware resources, and, on the other hand, the parallel and the optimized architectures, which offer a much reduced, time execution. In order to benefit from the advantages of the allocated hardware resources and the classification of the runtime process, these four architectures have been implemented on field programmable gate array Virtex-6 devices and their performances were quantified and evaluated according to a cost criterion. The energy dissipated by each of these architectures was measured; the achieved results have allowed us to conclude that the serial optimized architecture is the optimal solution, since it creates a tradeoff between the low cost, and the energy efficiency, and still real-time for the considered application.

Real-Time Implementation of Light-Independent Traffic Sign Recognition Approach

In order to guarantee the safety of road users (both pedestrians and drivers), one key element is Traffic Signs Recognition. Computer vision for driving assistance offered significant progress in road sign detection, but still needs great improvements because of difficulties associated with extreme variations in lighting conditions. When poor lighting conditions are met, the driver must be alerted when a road sign is encountered. This is feasible through an automatic system equipped with a camera on the dashboard of the vehicle, capable of detecting the road sign and alarming the driver. This chapter’s main objective is the development of an adequate and robust Traffic Signs Recognition system functioning independently of lighting situations. A three task approach is proposed, it is mainly composed of: object detection, shape classification and content classification. The detection phase is based on the RGB-color space segmentation with an empirically determined threshold. It extracts the relevant red and blue regions in the image with limit values of Bounding Boxes (BB). After object extraction, the sign’s shape is classified by an artificial neural network (ANN). Road signs are classified according to their shape characteristics, as triangular, squared and circular shapes. The classified circular and triangular shapes are passed on to the second ANN in the third phase. These identify the pictogram of the road sign. The output of the second ANN allows the full recognition of the traffic sign. The algorithm proposed and its performances are tested and discussed in a dataset of real driving scenarios which captured in various weather conditions.

Design of efficient embedded system for road sign recognition

Automatic traffic sign recognition enhances driver interactivity while driving. It improves the vigilance of the driver by alarming-him/her of signs that he/she may not perceive. In this paper, an embedded real-time system for automatic traffic sign recognition is proposed. The segmentation task of an acquired scene is processed in the HSV color space. The recognition process is performed by using the Oriented fast-and-Rotated Brief features. The developed algorithm is implemented on a ZedBoard hardware platform. The detection rate reaches the value of 97.39%. The recognition rate is equal to 95.53%.

Real-time hardware/software co-design of a traffic sign recognition system using Zynq FPGA

This paper describes a hardware implementation for real-time road signs recognition system on automotive oriented FPGA. The proposed traffic sign recognition system is based on color segmentation and Template Matching. This architecture is implemented on FPGA device of ZYNQ 7020 Xilinx family. Therefore, a software/hardware co-design architecture for a Zynq-7020 FPGA is presented as a primary objective of this work. Results show that the proposed system achieves over 97% accuracy even in difficult condition weather. In addition, in this work, a hardware implementation of the proposed system will be presented to achieve real-time constraints.