====== Dynamic Vision Sensor (DVS) ====== ====Technology Overview==== Conventional vision sensors see the world as a series of frames. Successive frames contain enormous amounts of redundant information, wasting memory access, RAM, disk space, energy, computational power and time. In addition, each frame imposes the same exposure time on every pixel, making it difficult to deal with scenes containing very dark and very bright regions. The Dynamic Vision Sensor (DVS) solves these problems by using patented technology that works like your own retina. Instead of wastefully sending entire images at fixed frame rates, only the local pixel-level changes caused by movement in a scene are transmitted – //at exactly the time they occur//. The result is a stream of events at microsecond time resolution, equivalent to or better than conventional high-speed vision sensors running at thousands of frames per second. Power, data storage and computational requirements are also drastically reduced, and dynamic sensor range is increased by orders of magnitude due to the local processing. ====Application Areas==== *Surveillance and ambient sensing *Robotics: real-time, mobile, fixed *Laboratory and factory automation *Microscopy *Motion analysis, e.g. sports *Hydrodynamics *Sleep research or monitoring *Fluorescent imaging *Tracking ====Advantages==== ^ Conventional high-speed vision systems ^ DVS ^ DVS Benefits ^ | Requires fast PC | Works with any laptop | Lower costs\\ Lower power consumption | | Extremely large data storage (often several TB)\\ Highly redundant data | Low storage requirements\\ No redundant data | Lower costs\\ More portable\\ Easier and faster data management | | Custom interface cards | Webcam-sized, USB2.0\\ Java API | More portable\\ Easier programming | | Batch-mode acquisition\\ Off-line post-processing | Real-time acquisition\\ Extremely low latency | Continuous processing\\ No downtime, lower costs | | Low dynamic range, ordinary sensitivity\\ Needs special bright lighting (lasers, strobes, etc.) for short exposure times | High sensitivity\\ No special lighting needed | Lower costs\\ Simpler data acquisition | | Limited dynamic range, typically 50 dB | Very high dynamic range (120 dB) | Usable in more real-world situations | ====Case Studies==== ===Case Study 1: Vision In Challenging Environments=== **Problem:** You need to dynamically recognize objects in an environment with very bright lights and dark shadows. Conventional video cameras either over-expose one part of the scene or under-expose another part of the scene, losing important objects in the process **Solution:** The DVS sensor automatically adapts to differing lighting conditions in different parts of an image without any calibration. Its high dynamic range brings out details that could not be detected with conventional vision systems. ===Case Study 2: Fluid Particle Image Velocimetry=== **Problem:** You are analyzing turbulent fluid flow. Your conventional high-speed vision setup requires a cumbersome and expensive high-speed PC, lots of hard disk space, custom interface cards and high-intensity laser strobe lighting to illuminate the fluid. After each test run you have to wait minutes or hours while the data is processed. **Solution:** DVS sensors enable you to replace your entire system with a single standard PC with a USB connection. Only normal collimated light is required to illuminate the fluid. The small data flow can be processed in real time, enabling you to work continuously and even adjust experimental parameters on the fly. ===Case Study 3: Real-Time Robotics=== **Problem:** You are deploying a mobile robot that must work in the real world. You are operating under tight constraints of power consumption, space and weight. Conventional vision processing systems consume far too much power to fit on the robot platform. The only alternative is to send the images for off-line processing, but this would require a separate server, increase response times and limit the range of the robot. **Solution:** The DVS vision sensor does all of the front-end processing, giving you only the “interesting” events in a scene. You can integrate all of your processing hardware on-board. ===== Specifications ===== | Functionality|Asynchronous temporal contrast | | Pixel size um (lambda|40x40 (200x200) | | Fill factor (%)|8.1% (PD area 151μm2) | | Fabrication process|4M 2P 0.35um standard CMOS | | Pixel complexity|26 transistors (14 analog), 3 capacitors | | Array size|128x128 (higher resolutions coming soon) | | Die size mm2|6.0 x 6.3 | | Chip interface|15-bit word-parallel AER\\ active low req and ack | | Computer interface|USB 2.0, Windows XP driver\\ [[http://jaer.wiki.sourceforge.net|Java API]] & Matlab output file format | | Power consumption|Chip: 24mW @ 3.3V\\ 1.5mA core\\ 0.3mA logic\\ 5.5mA biases\\ USB System: approx. 70mA | | Dynamic range|120dB\\ 2 lux to > 100 klux scene illumination with f/1.2 lens | | Photodiode dark current at room temperature|4fA (~10nA/cm2)\\ Nwell photodiode | | Response latency|15μs @ 1 klux chip illumination | | Max events/sec|~1M events/sec | | FPN, matching|2.1% contrast | | Optics | Standard C-mount lenses\\ Other custom mounts available | ====Contact==== Institute of Neuroinformatics\\ Winterthurerstr. 190\\ 8057 Zürich\\ Switzerland\\ Tel. +41-1-635 3051\\ Fax +41-1-635 3053\\ <info@ini.phys.ethz.ch>\\ [[http://www.ini.uzh.ch]]
