Abnormal Scene Change Detection from a Moving Camera Using Bags of Patches and Spider-Web Map

Jun-Wei Hsieh, Chi Hung Chuang, Salah Alghyaline, Hui Fen Chiang, Chao Hong Chiang

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


This paper proposes a novel surveillance system for detecting exceptional scene changes as abnormal events with a mobile camera mounted on a robot. In contrast to abnormal event analysis using fixed cameras, three key problems should be tackled in this system, i.e., scene construction, robot localization, and scene comparison. For the first problem, scene construction, a clustering scheme is proposed for extracting a set of key frames from the surveillance environment. Each key frame is further divided into a set of patches, which forms a sparse representation for representing scene contents. In addition to the compression effect, the scheme can tackle the effects of misalignment and lighting changes well. For the localization problem, a novel patch matching method is proposed to reduce not only the size of the search space but also the size of the feature dimensions in similarity matching. To prune the search space, a set of projection kernels is used to construct a ring structure. Then, one order of time complexity in the similarity calculation can be reduced from the structure. After scene searching, the robot location is not always guaranteed to be successfully registered to the scene map. Thus, a novel spider-web map is proposed to tackle the effect of misalignment and then detect different exceptional scene changes from the videos. The proposed method has been rigorously tested on a variety of videos to demonstrate its superiority in object detection and abnormal scene change detection.

Original languageEnglish
Article number6985585
Pages (from-to)2866-2881
Number of pages16
JournalIEEE Sensors Journal
Issue number5
StatePublished - 26 May 2015


  • abnormal scene change detection
  • behavior analysis
  • pattern matching
  • video surveillance


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