Pervasive deployment of surveillance cameras today poses enormous scalability challenges to video analytics systems operating over many camera feeds. Currently, there are few indexing tools to organize video feeds beyond what is provided by a standard file system.
Recent video analytic systems implement application-specific frame profiling and sampling techniques to reduce the number of raw videos processed, leveraging frame-level redundancy or manually labeled spatial-temporal correlation between cameras. This paper presents Video-zilla, a standalone indexing layer between
video query systems and a video store to organize video data. We propose a video data unit abstraction, semantic video stream (SVS), based on a notion of distance between objects in the video. SVS implicitly captures scenes, which is missing from current video content characterization and a middle ground between individual
frames and an entire camera feed. We then build a hierarchical index that exposes the semantic similarity both within and across camera feeds, such that Video-zilla can quickly cluster video feeds based on their content semantics without manual labeling. We implement and evaluate Video-zilla in three use cases: object identification queries, clustering for training specialized DNNs, and archival services. In all three cases, Video-zilla reduces the time complexity
of inter-camera video analytics from linear with the number of cameras to sublinear, and reduces query resource usage by up to 14x compared to using frame-level or spatial-temporal similarity built into existing query systems.
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This content will become publicly available on June 27, 2024
STAR: A Cache-based Stream Warehouse System for Spatial Data
The proliferation of mobile phones and location-based services has given rise to an explosive growth in spatial data. In order to enable spatial data analytics, spatial data needs to be streamed into a data stream warehouse system that can provide real-time analytical results over the most recent and historical spatial data in the warehouse. Existing data stream warehouse systems are not tailored for spatial data. In this paper, we introduce the
- NSF-PAR ID:
- 10467778
- Editor(s):
- Aref, Walid G.
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- ACM Transactions on Spatial Algorithms and Systems
- ISSN:
- 2374-0353
- Subject(s) / Keyword(s):
- ["spatial data","data stream","warehouse system","distributed system"]
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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