  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.

Computer Vision and Pattern Recognition;Machine Learning;Image and Video Processing