Unsupervised Abnormality Detection Using Heterogeneous Autonomous Systems

• URL: http://arxiv.org/abs/2006.03733v2
• Date: Tue, 14 Jul 2020 16:34:23 GMT
• Title: Unsupervised Abnormality Detection Using Heterogeneous Autonomous Systems
• Authors: Sayeed Shafayet Chowdhury, Kazi Mejbaul Islam and Rouhan Noor
• Abstract summary: Anomaly detection in a surveillance scenario is an emerging and challenging field of research. In this paper, a heterogeneous system is proposed which estimates the degree of abnormality of an unmanned surveillance drone. The proposed method performs satisfactorily on the IEEE SP Cup-2020 dataset with an accuracy of 97.3%.
• Score: 0.3867363075280543
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Anomaly detection (AD) in a surveillance scenario is an emerging and challenging field of research. For autonomous vehicles like drones or cars, it is immensely important to distinguish between normal and abnormal states in real-time. Additionally, we also need to detect any device malfunction. But the nature and degree of abnormality may vary depending upon the actual environment and adversary. As a result, it is impractical to model all cases a-priori and use supervised methods to classify. Also, an autonomous vehicle provides various data types like images and other analog or digital sensor data, all of which can be useful in anomaly detection if leveraged fruitfully. To that effect, in this paper, a heterogeneous system is proposed which estimates the degree of abnormality of an unmanned surveillance drone, analyzing real-time image and IMU (Inertial Measurement Unit) sensor data in an unsupervised manner. Here, we have demonstrated a Convolutional Neural Network (CNN) architecture, named AngleNet to estimate the angle between a normal image and another image under consideration, which provides us with a measure of anomaly of the device. Moreover, the IMU data are used in autoencoder to predict abnormality. Finally, the results from these two algorithms are ensembled to estimate the final degree of abnormality. The proposed method performs satisfactorily on the IEEE SP Cup-2020 dataset with an accuracy of 97.3%. Additionally, we have also tested this approach on an in-house dataset to validate its robustness.

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