Samir Bouaziz

A Smart Sensor for Image Processing: Towards a System on Chip

One of the solutions to reduce the computational complexity of image processing is to perform some low-level computations on the sensor focal plane. This paper presents a vision system based on a smart sensor. PARISl (programmable analog retina-like image sensor) is the first prototype used to evaluate the architecture of an on-chip vision system based on such a sensor and a digital processor. The sensor integrates analog and digital computing units. This architecture makes the vision system more compact and increases the performances reducing the data flow exchanges with the digital processor. A system has been implemented as a proof-of-concept. This has enabled us to evaluate the performance needed for a possible implementation of a digital processor on the same chip. The approach is compared to two architectures implementing CMOS sensors and interfaced to the same processor. The comparison is related to image processing computation time, processing reliability, programmability, precision, bandwidth and subsequent stages of computations

On chip vision system architecture using a CMOS retina

This paper discusses design solution for integrating a complete vision system on a single chip. We describe the architecture and the implementation of a smart integrated retina based vision system dedicated for vehicles applications. The retina is a circuit that combines image acquisition and analog/digital processing operators allowing the achievement of real-time image processing. Interests of vision system integration are analysed through comparisons with conventional approaches using CCD cameras and a digital processor or CMOS sensors combined with wired algorithms on FPGA technology. Our solution takes the advantages of both these solutions.

Image Processing Vision Systems: Standard Image Sensors Versus Retinas

To decrease the computational complexity of computer vision algorithms, one of the solutions is to achieve some low-level image processing on the sensor focal plan. It becomes a smart sensor device called a retina. This concept makes the vision systems more compact. It increases performances thanks to the reduction of data flow exchanges with external circuits. This paper presents a comparison relating two different vision system architectures. The first one implements a logarithmic complimentary metal-oxide-semiconductor (CMOS)/active pixel sensor interfaced to a microprocessor, where all computations are carried out. The second involves a CMOS sensor including analog processors allowing on-chip image processing. An external microprocessor is used to control the on-chip data flow and integrated operators. We have designed two vision systems as proof-of-concept. The comparison is related to image processing computation time, processing reliability, programmability, precision, bandwidth, and subsequent stages of computations.

PICAR: experimental platform for road tracking applications

The PICAR platform is an electrical car including an embedded electronics system. The generic goal is to design an embedded multi sensor plat-form for automotive application such as collision avoidance. Therefore the system includes classical sensors like video camera and ultrasonic sensors, a PC bi-processors, a CAN network and dedicated software for signal and image processing, data fusion and decision system. This plat-form allows experimenting customized sensors and specific architectures dedicated to fusion systems and data processing. The target scenarios are collision avoidance system, automatic parking, and lateral control application on a road lane.

Real-time characterization of Microsoft Flight Simulator 2004 for integration into Hardware In the Loop architecture

The purpose of this paper is to describe a real time characterization of FS2004 by measuring the dynamic model rate and quantify its temporal stability. This is done to evaluate Flight Simulator 2004 for a potential integration into HIL (Hardware In the Loop architecture) architecture used in developing embedded systems on UAVs. After a general presentation of the simulator and the reasons which lead us to study it, we describe the experimental procedure based on the definition of real-time simulation and Monte Carlo Method. Experiments conducted in different configurations allowed to isolate parameters that have a decisive impact on the real-time performances of the simulator.

Customizing CPU Instructions for Embedded Vision Systems

This paper presents the customization of two processors: the Altera NIOS2 and the Tensilica Xtensa, for fundamental algorithms in embedded vision systems: the salient point extraction and the optical flow computation. Both can be used for image stabilization, for drones and autonomous robots. Using 16-bit floating-point instructions, the architecture optimization is done in terms of accuracy, speed and power consumption. A comparison with a PowerPC Altivec is also done.Currently, television sets with flat plasma and LCD screens with improved resolutions and better color quality are emerging. To fully utilize their capabilities, lower resolution standard definition video material is enhanced. During such process, existing noise can become clearly visible, or additional artifacts may be introduced. These impairments are usually better visible in smooth image areas such as sky regions, motivating the development of special techniques for their removal. In this paper, we introduce a hardware accelerator for an existing pixel-accurate and spatially-consistent sky-detection algorithm. We describe the algorithmic and architectural design considerations of a resource-efficient real-time system, targeting an FPGA platform. Our results show that it is feasible to implement a simplified algorithm version by using only 5,756 logic-and 23,687 memory elements of the targeted device. A demonstrator setup using real-time camera signal, proves that images of up to 640times480 at a frame rate of 30 fps can be processed. Furthermore, according to our estimations, images with pixel rates up to 142 MHz, e.g. high definition TV, can be processed by the proposed system.

Image processing vision system implementing a smart sensor

There are many vision algorithms of image processing with CMOS image sensors such as image enhancement, segmentation, feature extraction and pattern recognition. These algorithms are frequently used in software-based operations. Here, the main research interest focuses on how to integrate image processing (vision) algorithms with CMOS integrated systems or how to implement smart retinas in hardware, in terms of their system-level architecture and design methodologies. The approach is compared with state-of-the-art vision chips build using digital SIMD arrays and vision systems using CMOS sensors combined with FPGA technology. As a test bench, an advanced algorithm for an exposure time calibration is presented with experimental results of image processing.

A real-time implementation of the Total Focusing Method for rapid and precise diagnostic in non destructive evaluation

This paper describes a multi-FPGA architecture dedicated for the real-time imaging using the Total Focusing Method (TFM) and an advanced acquisition technique called the Full Matrix Capture (FMC). The maximum operating frequency is 147.3 MHz for the control blocks and 161.3 MHz for the acquisition blocks. The architecture is able to perform real-time image reconstruction at a maximum frame rate of 73 fps and a maximum resolution of 128 × 128 pixels, which is adequate as a performance for a real-time imaging system with a good characterization of defects.

Naturalistic study of riders' behaviour in lane-splitting situations

Abstract This paper presents a naturalistic study of motorcyclists’ behaviours during their commutes in the Paris region. The study focuses on lane splitting, which consists of riding between two streams of slow or stopped vehicles that are proceeding in the same direction. This practice is frequent in dense traffic on French urban expressways and particularly in the Paris region but remains without detailed scientific analysis. In an ergonomic approach, 11 motorcyclists drove for a month with an instrumented vehicle with cameras. The video recordings enabled the description of the driving contexts and the conduct of self-confrontation interviews. The results concern firstly the description of the practice of lane splitting and its significance in their daily journeys. Secondly, the findings indicate the importance of perceptual activities in the participants’ riding behaviour. These perceptual activities are organized around two major processes: (1) an intensive search for information in the environment in order to foresee risky situations and (2) the detection of situations that systematically impair the visual conspicuity of the riders and taking steps to improve it. The findings are discussed with a view to gaining a better understanding of lane-splitting practices. At the time of writing, the results are also being used by the French authorities to improve the training curriculum and modify the legislation that deals with lane splitting.

Designing embedded systems for fixed-wing UAVs: Dynamic models study for the choice of an emulation vehicle

Fixed-wing Unmanned Aerial Vehicles (UAVs) are a special class of UAVs which present many advantages notably long range of action. Whereas, design of this kind of UAVs requires heavy logistics like outdoor tests, runways, and experimented pilots. These constraints reverberate on the design of embedded systems for fixed-wing UAVs. Because static tests are not representative, this paper proposes a practical approach to evaluate an embedded system on an appropriate vehicle emulating the dynamic model of a fixed-wing aircraft. For that, a comparison between the dynamic model of fixed-wing aircraft, tank-type mobile robot, and a bicycle is achieved. We show that, contrary to trend in literature, a mobile robot is not the optimal choice to emulate a fixed-wing UAV. Indeed, supposing a motion without slip (and a constant altitude for the aircraft), translation models of the three vehicles are under the form of Dubin car model. Whereas, translation and rotation velocities of tank-type mobile robot are coupled (while it is not the case for the aircraft where propulsion and turning are actuated separately). This constraint defines an allowed kinematic zone which limits the emulation of a fixed wing airplane. In the other hand, in bicycle model “bank to turn effect” is similar to the one observed in fixed-wing aircraft model. Furthermore, both models are not defined when the translation velocity tends to zero (stalling effect). As a conclusion, we propose to use mobile robot to test the navigation layer, and the bicycle to evaluate the sensor processing layer of an embedded system based fixed-wing UAVs applications.