Abstract
Advances in Deep Neural Network (DNN) techniques have revolutionized video analytics and unlocked the potential for querying and mining video event patterns. This paper details GNOSIS, an event processing platform to perform near-real-time video event detection in a distributed setting. GNOSIS follows a serverless approach where its component acts as independent microservices and can be deployed at multiple nodes. GNOSIS uses a declarative query-driven approach where users can write customize queries for spatiotemporal video event reasoning. The system converts the incoming video streams into a continuous evolving graph stream using machine learning (ML) and DNN models pipeline and applies graph matching for video event pattern detection. GNOSIS can perform both stateful and stateless video event matching. To improve Quality of Service (QoS), recent work in GNOSIS incorporates optimization techniques like adaptive scheduling, energy efficiency, and content-driven windows. This paper demonstrates the Occupational Health and Safety query use cases to show the GNOSIS efficacy.
Highlights
The event processing domain focuses on mining patterns from the data stream in a timely fashion
Deep learning techniques have achieved a breakthrough in resolving fundamental video analytics issues such as object detection and classification
The recent works in video analytics focus on specific techniques such as video querying [3, 5, 13, 16], resource efficiency [12, 19], offline optimizations and video pipelines [10]
Summary
The event processing domain focuses on mining patterns from the data stream in a timely fashion. The event processing systems continuously monitor the incoming stream and generate alerts and notifications whenever an interested pattern is detected. These systems are distributed, decoupled, and are characterized by lowlatency, high throughput and real-time performance [8, 11]. As per the Bureau of Labor Statistics (BLS), lack of safety helmets resulted in 84% of head injuries among workers These sites are located at remote places and are monitored using closed-circuit cameras. There can be other scenarios like counting the number of workers and managers for a specific day Performing such event-driven tasks leads to multiple challenges such as unstructured video representation, event querying, deploying video pipelines, distributed deployment, and event reasoning.
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