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Unsupervised Adaptive Multi-Object Tracking-by-Clustering Algorithm With a Bio-Inspired System
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DOI：10.1109/ACCESS.2022.3154895.png)
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Abstract
En 中文
A problem of current interest is how to emulate nature by acquiring information in a neuromorphic-like fashion; namely, by using configurable hardware and electronic systems to emulate the information gathering and processing strategies of biological systems. In this paper, we introduce BioCAMSHIFT, an algorithm for a bio-inspired system that acquires information via a neuromorphic process and uses it to track multiple objects. The system consists of a silicon retina that simulates the behavior of the human eye together with a communication system that uses an Address-Event Representation protocol to transmit information in a way analogous to that of biological neural systems. An unsupervised procedure, based on the CAMSHIFT algorithm, is then used for multi-object tracking. It takes advantage of the retina's high event rate to adapt to the changing sizes of the objects in its field of view. The proposed system has been experimentally validated using a data set from Freeway 210 in Pasadena, California, demonstrating a significantly better improvement in terms of multi-vehicle detection and tracking performance over the current state of the art.
Keywords:
Retina
Silicon
Robot sensing systems
Clustering algorithms
Neurons
Protocols
Neuromorphics
BioCAMSHIFT
address-event representation (AER)
CAMSHIFT
bio-inspired system
bioinformatics
silicon retina devices
clustering algorithms
neuromorphic system
jAER