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A Neuromorphic Approach for Tracking using Dynamic Neural Fields on a Programmable Vision-chip

Type of publication Peer-reviewed
Publikationsform Proceedings (peer-reviewed)
Author Martel Julien N.P., Sandamirskaya Yulia,
Project Biological Information in Cortical Communication
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Proceedings (peer-reviewed)

Title of proceedings ICDSC '16 Proceedings of the 10th International Conference on Distributed Smart Camera
Place Paris
DOI 10.1145/2967413.2967444


In artificial vision applications, such as tracking, a large amount of data captured by sensors is transferred to processors to extract information relevant for the task at hand. Smart vision sensors offer a means to reduce the computational 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 architecture of this device is optimized to perform local operations. To perform a task like tracking, we implement a neuromorphic approach using a Dynamic Neural Field, which allows to segregate, memorize, and track objects. Our system, consisting of the vision-chip running the DNF, outputs only the activity that corresponds to the tracked objects. These outputs reduce the bandwidth needed to transfer information as well as further post-processing, since computation happens at the pixel level.