VideoScoop: A Non-Traditional Domain-Independent Framework For Video Analysis
This work addresses the need for automated, domain-independent video analysis for applications like surveillance and assisted living, though it appears incremental as it builds on existing content extraction and graph methods.
The authors tackled the problem of automating video situation analysis, which is currently manual or domain-specific, by proposing a general-purpose framework that uses relational and graph models to detect activities across domains. They reported extensive experiments across three domains, showing the approach's accuracy, efficiency, and robustness.
Automatically understanding video contents is important for several applications in Civic Monitoring (CM), general Surveillance (SL), Assisted Living (AL), etc. Decades of Image and Video Analysis (IVA) research have advanced tasks such as content extraction (e.g., object recognition and tracking). Identifying meaningful activities or situations (e.g., two objects coming closer) remains difficult and cannot be achieved by content extraction alone. Currently, Video Situation Analysis (VSA) is done manually with a human in the loop, which is error-prone and labor-intensive, or through custom algorithms designed for specific video types or situations. These algorithms are not general-purpose and require a new algorithm/software for each new situation or video from a new domain. This report proposes a general-purpose VSA framework that overcomes the above limitations. Video contents are extracted once using state-of-the-art Video Content Extraction technologies. They are represented using two alternative models -- the extended relational model (R++) and graph models. When represented using R++, the extracted contents can be used as data streams, enabling Continuous Query Processing via the proposed Continuous Query Language for Video Analysis. The graph models complement this by enabling the detection of situations that are difficult or impossible to detect using the relational model alone. Existing graph algorithms and newly developed algorithms support a wide variety of situation detection. To support domain independence, primitive situation variants across domains are identified and expressed as parameterized templates. Extensive experiments were conducted across several interesting situations from three domains -- AL, CM, and SL-- to evaluate the accuracy, efficiency, and robustness of the proposed approach using a dataset of videos of varying lengths from these domains.