Browsing by Subject "View-based querying"

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Open Access
A scenario-based query processing framework for video surveillance
(2009) Şaykol, Ediz
Video surveillance has become one of the most interesting and challenging application areas in video processing domain. Automated access to the semantic content of surveillance videos to detect anomalies is among the basic tasks; however due to the high variability of the visual features and large size of the video input, it still remains a challenging issue. A considerable amount of research dealing with automated access to video surveillance has appeared in the literature; however, signiﬁcant semantic gaps in event models and content-based access still remain. In this thesis, we propose a scenario-based query processing framework for video surveillance archives, especially for indoor environments. A scenario is speciﬁed as a sequence of event predicates that can be enriched with object-based low—level features and directional predicates. We also propose a keyframe labeling technique, which assigns labels to keyframes extracted based on keyframe detection algorithm, hence transforms the input video to an event sequence based representation. The keyframe detection scheme relies on an inverted tracking scheme, which is a view-based representation of the actual content by an inverted index. We also devise mechanisms based on ﬁnite state automata using this event sequence representation to detect a typical set of anomalous events in the scene, which are also used for meta-data extraction. Our query processing framework also supports inverse querying and view-based querying, for after-the-fact activity analysis, since the inverted tracking scheme effectively tracks the moving objects and enables view-based addressing of the scene. We propose a speciﬁc surveillance query language to express the supported query types in a scenario-based manner. We also present a visual query speciﬁcation interface devised to enhance the query-speciﬁcation process. It has been shown through performance experiments that the keyframe labeling algorithm signiﬁcantly reduces the storage requirements and yields a reasonable anomaly detection performance. We have also conducted performance experiments to show that our query processing technique has a high expressive power and satisfactory retrieval accuracy in video surveillance.