Abstract:
Due to the widespread deployment of Surveillance Systems and IoT applications, the amount of surveillance data is massively on the rise. Storing and analyzing video surveillance data is a significant challenge, requiring video interpretation and event detection along with related context.
Low-level features from multimedia content are extracted and represented in symbolic form. These features include shape, texture, and color information of the multimedia content. In this work, a methodology is proposed, which extracts the salient features and properties using machine learning techniques typical of the surveillance domain, and represents the information using a domain ontology tailored explicitly for the detection of certain activities. An ontology is developed to include concepts and properties which may be applicable in the domain of surveillance and its applications. Extracted features are represented as Linked Data using an ontology. The proposed approach is validated with actual implementation and is thus evaluated by recognizing suspicious activity in an open parking space. The suspicious activity detection is formalized through inference rules and SPARQL queries. Eventually, Semantic Web Technology has proven to be a remarkable toolchain to interpret videos, thus opening novel possibilities for video scene representation, and detection of complex events, without any human involvement. The proposed novel approach can thus have representation of frame-level information of a video in structured representation and perform event detection while reducing storage and enhancing semantically-aided retrieval of video data. A video dataset of six different, and unusual, suspicious activities has also been built, which can be useful to address problems related to activity recognition in other smart parking scenarios and thus opens up plethora of use-cases as well.
