  In this paper we compare event-based decaying and time based-decaying memory
surfaces for high-speed eventbased tracking, feature extraction, and object
classification using an event-based camera. The high-speed recognition task
involves detecting and classifying model airplanes that are dropped free-hand
close to the camera lens so as to generate a challenging dataset exhibiting
significant variance in target velocity. This variance motivated the
investigation of event-based decaying memory surfaces in comparison to
time-based decaying memory surfaces to capture the temporal aspect of the
event-based data. These surfaces are then used to perform unsupervised feature
extraction, tracking and recognition. In order to generate the memory surfaces,
event binning, linearly decaying kernels, and exponentially decaying kernels
were investigated with exponentially decaying kernels found to perform best.
Event-based decaying memory surfaces were found to outperform time-based
decaying memory surfaces in recognition especially when invariance to target
velocity was made a requirement. A range of network and receptive field sizes
were investigated. The system achieves 98.75% recognition accuracy within 156
milliseconds of an airplane entering the field of view, using only twenty-five
event-based feature extracting neurons in series with a linear classifier. By
comparing the linear classifier results to an ELM classifier, we find that a
small number of event-based feature extractors can effectively project the
complex spatio-temporal event patterns of the dataset to an almost linearly
separable representation in feature space.
