Surveillance Video Querying With A Human-in-the-Loop

Date

2020-06-19

Author

STONEBROKER, MICHAEL
Bhargava, Bharat
Cafarella, Michael
COLLINS, ZACHARY
McClellan, Jenna
SIPSER, AARON
Sun, Tao
NESEN, ALİNA
SOLAIMAN, K.M.A.
MANI, GANAPATHY
Kochpatcharin, Kevin
Kochpatcharin, Kevin
Angın, Pelin
MACDONALD, JAMES

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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SurvQ is a video monitoring system appropriate for surveillance applications such as those found in security and law enforcement. It performs real time object property identification and stores all data in a scalable DBMS. Standing queries implemented as database triggers are supported. SurvQ contains novel adaptive machine learning and algorithmic property classification. The application of SurvQ to assist the West Lafayette (IN) police department at identifying suspects in video is described. This paper also describes the basics of the SurvQ architecture and its human-in-the-loop interface designed to accelerate everyday police investigations.

URI

https://hilda.io/2020/proceedings/HILDA2020_paper6.pdf
https://hdl.handle.net/11511/96840

Conference Name

Workshop on Human-In-the-Loop Data Analytics (HILDA)

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Department of Computer Engineering, Conference / Seminar

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Citation Formats

M. STONEBROKER et al., “Surveillance Video Querying With A Human-in-the-Loop,” presented at the Workshop on Human-In-the-Loop Data Analytics (HILDA), Amerika Birleşik Devletleri, 2020, Accessed: 00, 2022. [Online]. Available: https://hilda.io/2020/proceedings/HILDA2020_paper6.pdf.