Complex Event Processing (CEP) is an event processing paradigm to perform real-time analytics over streaming data and match high-level event patterns. Presently, CEP is limited to process structured data stream. Video streams are complicated due to their unstructured data model and limit CEP systems to perform matching over them. This work introduces a graph-based structure for continuous evolving video streams, which enables the CEP system to query complex video event patterns. We propose the Video Event Knowledge Graph (VEKG), a graph-driven representation of video data. VEKG models video objects as nodes and their relationship interaction as edges over time and space. It creates a semantic knowledge representation of video data derived from the detection of high-level semantic concepts from the video using an ensemble of deep learning models. A CEP-based state optimization — VEKG-Time Aggregated Graph (VEKG-TAG) — is proposed over VEKG representation for faster event detection. VEKG-TAG is a spatiotemporal graph aggregation method that provides a summarized view of the VEKG graph over a given time length. We defined a set of nine event pattern rules for two domains (Activity Recognition and Traffic Management), which act as a query and applied over VEKG graphs to discover complex event patterns. To show the efficacy of our approach, we performed extensive experiments over 801 video clips across 10 datasets. The proposed VEKG approach was compared with other state-of-the-art methods and was able to detect complex event patterns over videos with [Formula: see text]-Score ranging from 0.44 to 0.90. In the given experiments, the optimized VEKG-TAG was able to reduce 99% and 93% of VEKG nodes and edges, respectively, with 5.19[Formula: see text] faster search time, achieving sub-second median latency of 4–20[Formula: see text]ms.
Social health and emotional wellness is a matter of concern in today's urban world. Being the part of a metropolis has an effect on mental health through the influence of increased stressors and factors such as overcrowded and polluted environment, high levels of violence, and reduced social support. It is important to realize that only healthy citizens can constitute together a smart city. In this paper, we present a fuzzy-based approach for analyzing the well being of a person. We track the general day to day activities of a person and analyze its performance. To do so, we divide the factors affecting the wellness of a person into three components which are the physical, productive and social. Using these parameters, we output a coefficient for the overall well being of a person.
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