N-ImageNet: Towards Robust, Fine-Grained Object Recognition with Event Cameras

• URL: http://arxiv.org/abs/2112.01041v1
• Date: Thu, 2 Dec 2021 08:08:32 GMT
• Title: N-ImageNet: Towards Robust, Fine-Grained Object Recognition with Event Cameras
• Authors: Junho Kim, Jaehyeok Bae, Gangin Park, and Young Min Kim
• Abstract summary: We introduce N-ImageNet, a large-scale dataset targeted for robust, fine-grained object recognition with event cameras. N-ImageNet serves as a challenging benchmark for event-based object recognition, due to its large number of classes and samples.
• Score: 5.726662931271546
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: We introduce N-ImageNet, a large-scale dataset targeted for robust, fine-grained object recognition with event cameras. The dataset is collected using programmable hardware in which an event camera consistently moves around a monitor displaying images from ImageNet. N-ImageNet serves as a challenging benchmark for event-based object recognition, due to its large number of classes and samples. We empirically show that pretraining on N-ImageNet improves the performance of event-based classifiers and helps them learn with few labeled data. In addition, we present several variants of N-ImageNet to test the robustness of event-based classifiers under diverse camera trajectories and severe lighting conditions, and propose a novel event representation to alleviate the performance degradation. To the best of our knowledge, we are the first to quantitatively investigate the consequences caused by various environmental conditions on event-based object recognition algorithms. N-ImageNet and its variants are expected to guide practical implementations for deploying event-based object recognition algorithms in the real world.

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