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Asynchronous Spatiotemporal Spike Metric for Event Cameras.

Jianing Li, Yihua Fu, Siwei Dong

IEEE Transactions on Neural Networks and Learning Systems

|March 8, 2021

Summary

This study introduces a novel metric for measuring distances between asynchronous spikes from event cameras. The new method enhances vision tasks by accurately capturing spatiotemporal structure and polarity attributes.

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Area of Science:

Computer Vision
Bioinspired Sensors
Machine Learning

Background:

Event cameras offer high dynamic range and temporal resolution, mimicking biological vision.
Asynchronous spike data from event cameras are modeled using marked spatiotemporal point processes (MSTPPs).
A key challenge is quantifying distances between asynchronous spikes within MSTPPs.

Purpose of the Study:

To propose a general metric for measuring distances between asynchronous spikes from event cameras.
To address the limitations in current methods for analyzing event camera data.
To improve the representation of spatiotemporal and polarity information.

Main Methods:

Introduced conditional probability density functions to model spatiotemporal distribution and polarity priors.
Defined a spatiotemporal Gaussian kernel to represent spike data as continuous functions in a reproducing kernel Hilbert space (RKHS).
Quantified spike distances using inner products within the RKHS.

Main Results:

The proposed metric outperforms existing state-of-the-art methods in event camera data analysis.
Achieved significant improvements in computational efficiency.
Demonstrated superior capability in depicting changes related to spatiotemporal structure and polarity attributes.

Conclusions:

The developed asynchronous spatiotemporal spike metric effectively quantifies distances between event camera spikes.
This metric enhances the analysis of complex visual information captured by event cameras.
Offers a more robust and efficient approach for various vision tasks utilizing event camera data.