Event-based Vision: A Survey

Event cameras are bio-inspired sensors that differ from conventional frame cameras: Instead of capturing images at a fixed rate, they asynchronously measure per-pixel brightness changes, and output a stream of events that encode the time, location and sign of the brightness changes. Event cameras offer attractive properties compared to traditional cameras: high temporal resolution (in the order of microseconds), very high dynamic range (140 dB vs. 60 dB), low power consumption, and high pixel bandwidth (on the order of kHz) resulting in reduced motion blur. Hence, event cameras have a large potential for robotics and computer vision in challenging scenarios for traditional cameras, such as low-latency, high speed, and high dynamic range. However, novel methods are required to process the unconventional output of these sensors in order to unlock their potential. This paper provides a comprehensive overview of the emerging field of event-based vision, with a focus on the applications and the algorithms developed to unlock the outstanding properties of event cameras. We present event cameras from their working principle, the actual sensors that are available and the tasks that they have been used for, from low-level vision (feature detection and tracking, optic flow, etc.) to high-level vision (reconstruction, segmentation, recognition). We also discuss the techniques developed to process events, including learning-based techniques, as well as specialized processors for these novel sensors, such as spiking neural networks. Additionally, we highlight the challenges that remain to be tackled and the opportunities that lie ahead in the search for a more efficient, bio-inspired way for machines to perceive and interact with the world

FPGA synthesis of an stereo image matching architecture for autonomous mobile robots

This paper describes a hardware proposal to speed up the process of image matching in stereo vision systems like those employed by autonomous mobile robots. This proposal combines a classical window-based matching approach with a previous stage, where key points are selected from each image of the stereo pair. In this first step the key point extraction method is based on the SIFT algorithm. Thus, in the second step, the window-based matching is only applied to the set of selected key points, instead of to the whole images. For images with a 1% of key points, this method speeds up the matching four orders of magnitude. This proposal is, on the one hand, a better parallelizable architecture than the original SIFT, and on the other, a faster technique than a full image windows matching approach. The architecture has been implemented on a lower power Virtex 6 FPGA and it achieves a image matching speed above 30 fps.This work has been funded by Spanish government project TEC2015-66878-C3-2-R (MINECO/FEDER, UE)

Towards a Common Software/Hardware Methodology for Future Advanced Driver Assistance Systems

The European research project DESERVE (DEvelopment platform for Safe and Efficient dRiVE, 2012-2015) had the aim of designing and developing a platform tool to cope with the continuously increasing complexity and the simultaneous need to reduce cost for future embedded Advanced Driver Assistance Systems (ADAS). For this purpose, the DESERVE platform profits from cross-domain software reuse, standardization of automotive software component interfaces, and easy but safety-compliant integration of heterogeneous modules. This enables the development of a new generation of ADAS applications, which challengingly combine different functions, sensors, actuators, hardware platforms, and Human Machine Interfaces (HMI). This book presents the different results of the DESERVE project concerning the ADAS development platform, test case functions, and validation and evaluation of different approaches. The reader is invited to substantiate the content of this book with the deliverables published during the DESERVE project. Technical topics discussed in this book include:Modern ADAS development platforms;Design space exploration;Driving modelling;Video-based and Radar-based ADAS functions;HMI for ADAS;Vehicle-hardware-in-the-loop validation system

Implementing video compression algorithms on reconfigurable devices

The increasing density offered by Field Programmable Gate Arrays(FPGA), coupled with their short design cycle, has made them a popular choice for implementing a wide range of algorithms and complete systems. In this thesis the implementation of video compression algorithms on FPGAs is studied. Two areas are specifically focused on; the integration of a video encoder into a complete system and the power consumption of FPGA based video encoders. Two FPGA based video compression systems are described, one which targets surveillance applications and one which targets video conferencing applications. The FPGA video surveillance system makes use of a novel memory format to improve the efficiency with which input video sequences can be loaded over the system bus. The power consumption of a FPGA video encoder is analyzed. The results indicating that the motion estimation encoder stage requires the most power consumption. An algorithm, which reuses the intra prediction results generated during the encoding process, is then proposed to reduce the power consumed on an FPGA video encoder’s external memory bus. Finally, the power reduction algorithm is implemented within an FPGA video encoder. Results are given showing that, in addition to reducing power on the external memory bus, the algorithm also reduces power in the motion estimation stage of a FPGA based video encoder

Towards a Common Software/Hardware Methodology for Future Advanced Driver Assistance Systems

The European research project DESERVE (DEvelopment platform for Safe and Efficient dRiVE, 2012-2015) had the aim of designing and developing a platform tool to cope with the continuously increasing complexity and the simultaneous need to reduce cost for future embedded Advanced Driver Assistance Systems (ADAS). For this purpose, the DESERVE platform profits from cross-domain software reuse, standardization of automotive software component interfaces, and easy but safety-compliant integration of heterogeneous modules. This enables the development of a new generation of ADAS applications, which challengingly combine different functions, sensors, actuators, hardware platforms, and Human Machine Interfaces (HMI). This book presents the different results of the DESERVE project concerning the ADAS development platform, test case functions, and validation and evaluation of different approaches. The reader is invited to substantiate the content of this book with the deliverables published during the DESERVE project. Technical topics discussed in this book include:Modern ADAS development platforms;Design space exploration;Driving modelling;Video-based and Radar-based ADAS functions;HMI for ADAS;Vehicle-hardware-in-the-loop validation system