Adapting Stream Processing Framework for Video Analysis

Abstract

Stream processing (SP) became relevant mainly due to inexpensive and hence ubiquitous deployment of sensors in many domains (e.g., environmental monitoring, battle field monitoring). Other continuous data generators (surveillance, traffic data) have also prompted processing and analysis of these streams for applications such as traffic congestion/accidents and personalized marketing. Image processing has been researched for several decades. Recently there is emphasis on video stream analysis for situation monitoring due to the ubiquitous deployment of video cameras and unmanned aerial vehicles for security and other applications. This paper elaborates on the research and development issues that need to be addressed for extending the traditional stream processing framework for video analysis, especially for situation awareness. This entails extensions to: data model, operators and language for expressing complex situations, QoS (Quality of service) specifications and algorithms needed for their satisfaction. Specifically, this paper demonstrates inadequacy of current data representation (e.g., relation and arrable) and querying capabilities to infer long-term research and development issues.

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“…There is a growing demand for human resources to interpret the live data streams (e.g., from security cameras [13], [25], [53]). Numerous automated object detection algorithms have been investigated to atomize this process using statistical analysis [36] or machine learning (ML) [71] approaches.…”