AMAE: Adaptive Motion-Agnostic Encoder for Event-Based Object Classification
Yongjian Deng, Youfu Li, Hao Chen
Abstract
Event cameras, with low power consumption, high temporal resolution, and high dynamic range, have been used increasingly in computer vision. These superior characteristics enable event cameras to perform low-energy and high-response object classification tasks in challenging scenarios. Nevertheless, specific encoding methods for event-based classification are required owing to the unconventional output of event cameras. Existing event-based encoding methods have focused on extracting semantic and motion information in event signals. However, two main problems exist in these methods: <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$(i)$</tex-math></inline-formula> the motion information of event signals leads to mispredictions by the classifiers. <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$(ii)$</tex-math></inline-formula> effective evaluation methods to validate the motion robustness of event-based classification models have yet to be proposed. In this work, we introduce an adaptive motion-agnostic encoder for event streams to address the first problem. The proposed encoder would allow us to extract indistinguishable semantic information from an object in different motion circumstances. In addition, we propose a novel motion inconsistency evaluation method to assess the motion robustness of the classification models. We apply our method to several benchmark datasets and evaluate it using motion consistency and inconsistency testing methods. Classification performance shows that our proposed encoder outperforms state-of-the-art methods by a large margin.