Case studies and videos

Case Study 1: Fast vision in bad lighting

Problem: You need to react quickly to moving objects in uneven lighting conditions. Conventional video cameras are too slow and specialized high frame rate cameras produce too much data to process in real time. Both of these conventional solutions require very high and even lighting at high frame rate.

Solution: The DVS sensor nearly instantaneously reports movement of objects and 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 and its low data rate enables real time short latency processing at low CPU load.


DVS used for robotic goalie with 550 effective frames per second performance at 4% processor load. See robogoalie.

 For another example of fast robotics, see our pencil balancer website or directly the youtube video.

Case Study 2: Fluid Particle Tracking Velocimetry (PTV)

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.


DVS used for PTV, courtesy P. Hafliger, Univ. of Oslo. Related publicaton:

D. Drazen, P. Lichtsteiner, P. Hafliger, T. Delbruck, A. JensenToward real-time particle tracking using an event-based dynamic vision sensor,Experiments in Fluids, Vol. 51, 1465-1469, 2011

Case Study 3: Mobile Robotics

Problem: You are deploying a fast 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 much of the front-end processing, giving you only the “interesting” events in a scene at the time they occur. You can integrate all of your processing hardware on-board and react quickly to new input.


DVS data from driving.

Case Study 4: Sleep disorder research

Problem: You are studying sleep behavior patterns. Conventional video cameras record huge amounts of boring data where the subject is not moving, making it very labor intensive to manually annotate the behaviors.

Solution: The DVS only outputs subject movements. Instead of playing back the data at constant frame rate, you can play it back at constant event rate, so that the action is continuous. A whole night of sleep can be recorded in a 100 MB of storage and played back in less than a minute. Activity levels can be automatically extracted and any part of the recording can be viewed at 1 millisecond resolution.


DVS used to monitor mouse activity, courtesy I. Tobler, Univ. of Zurich.