A Neuromorphic Approach for Tracking using Dynamic Neural Fields on a Programmable Vision-chip Julien N.P. Martel Yulia Sandamirskaya Institute of Neuroinformatics Institute of Neuroinformatics University of Zurich and ETH Zurich University of Zurich and ETH Zurich 8057 Zurich, Switzerland 8057 Zurich, Switzerland
[email protected] [email protected] ABSTRACT DNF activation Saliency map In artificial vision applications, such as tracking, a large Frame amount of data captured by sensors is transferred to pro- Processor Array cessors to extract information relevant for the task at hand. Smart vision sensors offer a means to reduce the computa- Lens tional burden of visual processing pipelines by placing more processing capabilities next to the sensor. In this work, we use a vision-chip in which a small processor with memory is located next to each photosensitive element. The architec- Scene ture of this device is optimized to perform local operations. To perform a task like tracking, we implement a neuromor- phic approach using a Dynamic Neural Field, which allows to segregate, memorize, and track objects. Our system, con- sisting of the vision-chip running the DNF, outputs only the activity that corresponds to the tracked objects. These out- Cellular Processor Array Vision-chip puts reduce the bandwidth needed to transfer information as well as further post-processing, since computation happens Figure 1: An illustration of the system implemented at the pixel level. in this work: A vision-chip is directed at a scene and captures a stream of images processed on-chip CCS Concepts in a saliency map. This map is fed as an input to a •Computing methodologies ! Object detection; Track- Dynamic Neural Field (DNF), also run on-chip and ing; Massively parallel and high-performance simulations; used for tracking objects.