ANTShapes Benchmarking Datasets for Event-Based Neuromorphic Object Classification
Object classification in event-based computer vision is a task that is attracting considerable research attention. Event-based object classification is a fundamental task in the fields of security and applied computer vision, which typically use synchronous frame-based cameras and computing pipelines for operation. This approach has several practical flaws. The size, weight and power consumption of the device could prohibit deployment at the extreme edge or in covert sensing environments. Besides this, there are security concerns inherent in cloud-based or other off-device computation approaches due to the requirement of sending and receiving potentially sensitive data. Furthermore, this ...
| Authors | M. Middleton, H. Kayan, B. Sen Bhattacharya et al. |
|---|---|
| Primary category | cs.NE |
| Published | 27 August 2026 |
| Last updated at source | 27 August 2026 |
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