| Summary: | Anomaly detection aims to identify the data samples that do not conform to a known normal
(regular) behavior. As the definition of an anomaly is often ambiguous, unsupervised
and semi-supervised deep learning (DL) algorithms that primarily use unlabeled datasets
to model normal (regular) behaviors, are popularly studied in this context. The unmanned
aerial system (UAS) can use contextual anomaly detection algorithms to identify interesting
objects of concern in applications like search and rescue, disaster management, public
security etc. This thesis presents a novel multi-stage framework that supports detection
of frames with unknown anomalies, localization of anomalies in the detected frames, and
validation of detected frames for incremental semi-supervised learning, with the help of a
human operator. The proposed architecture is tested on two new datasets collected for a
UAV-based system. In order to detect and localize anomalies, it is important to both model
the normal data distribution accurately as well as formulate powerful discriminant (anomaly
scoring) techniques. We implement a generative adversarial network (GAN)-based anomaly
detection architecture to study the effect of loss terms and regularization on the modeling
of normal (regular) data and arrive at the most effective anomaly scoring method for the
given application. Following this, we use incremental semi-supervised learning techniques
that utilize a small set of labeled data (obtained through validation from a human operator),
with large unlabeled datasets to improve the knowledge-base of the anomaly detection
system. === Master of Science === Anomaly detection aims to identify the data samples that do not conform to a known normal
(regular) behavior. As the definition of an anomaly is often ambiguous, most techniques use
unlabeled datasets, to model normal (regular) behaviors. The availability of large unlabeled
datasets combined with novel applications in various domains, has led to an increasing interest in the study of anomaly detection. In particular, the unmanned aerial system (UAS)
can use contextual anomaly detection algorithms to identify interesting objects of concern
in applications like search and rescue (SAR), disaster management, public security etc. This
thesis presents a novel multi-stage framework that supports detection and localization of unknown anomalies, as well as the validation of detected anomalies, for incremental learning, with the help of a human operator. The proposed architecture is tested on two new datasets collected for a UAV-based system. In order to detect and localize anomalies, it is important to both model the normal data distribution accurately and formulate powerful discriminant (anomaly scoring) techniques. To this end, we study the state-of-the-art generative adversarial networks (GAN)-based anomaly detection algorithms for modeling of normal (regular) behavior and formulate effective anomaly detection scores. We also propose techniques to incrementally learn the new normal data as well as anomalies, using the validation provided by a human operator. This framework is introduced with the aim to support temporally critical applications that involve human search and rescue, particularly in disaster management.
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