EvAn: Neuromorphic Event-based Anomaly Detection

Abstract--Event-based cameras are bio-inspired novel sensors that asynchronously record changes in illumination in the form of events, thus resulting in significant advantages over conventional cameras in terms of low power utilization, high dynamic range, and no motion blur. Moreover, such cameras, by design, encode only the relative motion between the scene and the sensor (and not the static background) to yield a very sparse data structure, which can be utilized for various motion analytics tasks. We propose to model the motion dynamics in the event domain with dual discriminator conditional Generative adversarial Network (cGAN) built on state-of-the-art architectures. T o adapt event data for using as input to cGAN, we also put forward a deep learning solution to learn a novel representation of event data, which retains the sparsity of the data as well as encode the temporal information readily available from these sensors. Since there is no existing dataset for anomaly detection in event domain, we also provide an anomaly detection event dataset with an exhaustive set of anomalies. Index Terms --Neuromorphic Camera, Event data, Anomaly Detection, Generative Adversarial Network.null 1 I NTRODUCTION This paper focusses on anomaly detection using bio-inspired event-based cameras that register pixel-wise changes in brightness asynchronously in an efficient manner, which is radically different from how a conventional camera works. The asynchronous principle of operation endows event cameras [9] [10] [36] [41] to capture high-speed motions (with temporal resolution in the order of µs), high dynamic range ( 140 db) and sparse data. These low latency sensors have paved way to develop agile robotic applications [1], which was not feasible with conventional cameras.