ASAP: Adaptive Scheme for Asynchronous Processing of Event-based Vision Algorithms
This work addresses a specific bottleneck in event camera processing for robotics or real-time vision applications, representing an incremental improvement over existing packaging methods.
The paper tackles the problem of processing overflow and lack of responsivity in event-based vision algorithms by introducing ASAP, an adaptive scheme that uses variable-size packages to manage event streams. Experimental results show that ASAP enables responsive and efficient feeding of an asynchronous event-by-event clustering algorithm while preventing overflow.
Event cameras can capture pixel-level illumination changes with very high temporal resolution and dynamic range. They have received increasing research interest due to their robustness to lighting conditions and motion blur. Two main approaches exist in the literature to feed the event-based processing algorithms: packaging the triggered events in event packages and sending them one-by-one as single events. These approaches suffer limitations from either processing overflow or lack of responsivity. Processing overflow is caused by high event generation rates when the algorithm cannot process all the events in real-time. Conversely, lack of responsivity happens in cases of low event generation rates when the event packages are sent at too low frequencies. This paper presents ASAP, an adaptive scheme to manage the event stream through variable-size packages that accommodate to the event package processing times. The experimental results show that ASAP is capable of feeding an asynchronous event-by-event clustering algorithm in a responsive and efficient manner and at the same time prevents overflow.