EvAn: Neuromorphic Event-Based Sparse Anomaly Detection

Annamalai, L and Chakraborty, A and Thakur, CS (2021) EvAn: Neuromorphic Event-Based Sparse Anomaly Detection. In: Frontiers in Neuroscience, 15 .

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Abstract

Event-based cameras are bio-inspired novel sensors that asynchronously record changes in illumination in the form of events. This principle results in significant advantages over conventional cameras, such as low power utilization, high dynamic range, and no motion blur. Moreover, by design, such cameras encode only the relative motion between the scene and the sensor and not the static background to yield a very sparse data structure. In this paper, we leverage these advantages of an event camera toward a critical vision application�video anomaly detection. We propose an anomaly detection solution in the event domain with a conditional Generative Adversarial Network (cGAN) made up of sparse submanifold convolution layers. Video analytics tasks such as anomaly detection depend on the motion history at each pixel. To enable this, we also put forward a generic unsupervised deep learning solution to learn a novel memory surface known as Deep Learning (DL) memory surface. DL memory surface encodes the temporal information readily available from these sensors while retaining the sparsity of event data. Since there is no existing dataset for anomaly detection in the event domain, we also provide an anomaly detection event dataset with a set of anomalies. We empirically validate our anomaly detection architecture, composed of sparse convolutional layers, on this proposed and online dataset. Careful analysis of the anomaly detection network reveals that the presented method results in a massive reduction in computational complexity with good performance compared to previous state-of-the-art conventional frame-based anomaly detection networks. © Copyright © 2021 Annamalai, Chakraborty and Thakur.

Item Type:	Journal Article
Publication:	Frontiers in Neuroscience
Publisher:	Frontiers Media S.A.
Additional Information:	The copyright for this article belongs to Authors
Department/Centre:	Division of Electrical Sciences > Electronic Systems Engineering (Formerly Centre for Electronic Design & Technology) Division of Interdisciplinary Sciences > Computational and Data Sciences
Date Deposited:	20 Nov 2021 11:30
Last Modified:	20 Nov 2021 11:30
URI:	http://eprints.iisc.ac.in/id/eprint/69848

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