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Unsupervised surveillance video retrieval based on human action and appearance
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.ORCID iD: 0000-0002-5750-9655
2014 (English)In: Proceedings - International Conference on Pattern Recognition, LOS ALAMITOS: IEEE Computer Society, 2014, 4630-4635 p.Conference paper, Published paper (Refereed)
Abstract [en]

Forensic video analysis is the offline analysis of video aimed at understanding what happened in a scene in the past. Two of its key tasks are the recognition of specific actions, e.g., walking or fighting, and the search for specific persons, also referred to as re-identification. Although these tasks have traditionally been performed manually in forensic investigations, the current growing number of cameras and recorded video leads to the need for automated analysis. In this paper we propose an unsupervised retrieval system for surveillance videos based on human action and appearance. Given a query window, the system retrieves people performing the same action as the one in the query, the same person performing any action, or the same person performing the same action. We use an adaptive search algorithm that focuses the analysis on relevant frames based on the inter-frame difference of foreground masks. Then, for each analyzed frame, a pedestrian detector is used to extract windows containing each pedestrian in the scene. For each detection, we use optical flow features to represent its action and color features to represent its appearance. These extracted features are used to compute the probability that the detection matches the query according to the specified criterion. The algorithm is fully unsupervised, i.e., no training or constraints on the appearance, actions or number of actions that will appear in the test video are made. The proposed algorithm is tested on a surveillance video with different people performing different actions, providing satisfactory retrieval performance.

Place, publisher, year, edition, pages
LOS ALAMITOS: IEEE Computer Society, 2014. 4630-4635 p.
Keyword [en]
Adaptive optics, Feature extraction, Information retrieval, Monitoring, Motion estimation, Pattern recognition, Automated analysis, Forensic investigation, Inter-frame differences, Off-line analysis, Re identifications, Retrieval performance, Retrieval systems, Surveillance video, Security systems
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:kth:diva-167873DOI: 10.1109/ICPR.2014.792ISI: 000359818004129Scopus ID: 2-s2.0-84919904375ISBN: 9781479952083 (print)OAI: oai:DiVA.org:kth-167873DiVA: diva2:820299
Conference
22nd International Conference on Pattern Recognition, ICPR 2014, 24 August 2014 through 28 August 2014
Note

QC 20150611

QC 20150914

Available from: 2015-06-11 Created: 2015-05-22 Last updated: 2016-12-05Bibliographically approved

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Kjellström, Hedvig

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CiteExportLink to record
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Citation style
  • apa
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Output format
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