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Talk 2010-monash-seminar-panic-driven-event-detection

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  • 1. Panic-driven Event Detection from Surveillance Video Stream without Track and Motion Features Mahfuzul Haque and Manzur Murshed
  • 2. Presentation Outline • Introduction – Area – Problem – Objective • Event Detection • The Idea – Why not track or motion features? • The Proposed Method • Experimental Results • Q&A
  • 3. Research Area Dynamic Scene Understanding Stage 1 Video Stream     Stage 2 … Real-time Processing Event Detection Action / Activity Recognition Behaviour Recognition Behaviour Profiling Event Model Analytics  Intelligent Video Surveillance  Automated Alert  Smart Monitoring  Context-aware Environments
  • 4. The Problem Dynamic Scene Understanding Stage 1 Video Stream Stage 2 … Real-time Processing  Scene specific tuning  Availability of training data Large Surveillance Network  Thousands of video feeds  Ad-hoc remote surveillance  Dynamic scene variations Event Model Analytics How to develop a generic scene understanding framework that would reliably work on a wider range of scenarios?
  • 5. Research Objectives Dynamic Scene Understanding Stage 1 Video Stream Stage 2 … Real-time Processing Event Model Analytics  A generic scene understanding framework  Developing the building blocks for the essential processing stages  Scope:  Panic-driven abnormality detection  A fixed set of specific events
  • 6. Event Detection time     Specific types of events vs. abnormality An event persists for a certain duration of time The duration is variable Event characteristics of the same event  Variable in the same environment How to identify the generic  Variable from one scene to other characteristics of an event?
  • 7. The Idea f1 f2 f3 . . . time Frame-level Features     Event Model fn Temporal Features Classifier Event detection as temporal data classification problem A distinct set of temporal features can characterise an event Which/how frame-level features are extracted? How the observed frame-level features are transformed in temporal-features?
  • 8. The Idea Motion based approaches Tracking based approaches  Key points detection  Point matching in successive frames  Flow vectors: position, direction, speed  Object detection  Object matching in successive frames  Trajectories: object paths Common characteristics  Inter-frame association  Context specific information  Event models are not generic Hu et al. (ICPR 2008) Proposed generic approach  Object detection  Global frame-level descriptor: independent of scene characteristics Xiang et al. (IJCV 2006)  No Inter-frame association  Independent frame-level features => temporal features considering speed and temporal order
  • 9. The Idea f1 f2 f3 . . . time Frame-level Features Event Model fn Temporal Features Classifier Summary  Object based approach  Independent frame-level features– no object / position specific information, no spatial association  Frame-level features are transformed into temporal features considering speed and temporal order  Supposed to be more context invariant
  • 10. The Proposed Method Architecture Foreground Detector Frame-level Feature Extractor Temporal Feature Extractor Event Models Model Training Frame-level Feature Extraction (30 features) Background Subtraction Labelled frames Temporal Feature Extraction (270 features) Feature Ranking and Selection Event Model Training Foreground blobs Real-time Execution Selective Frame-level Feature Extraction Background Subtraction Incoming frames Foreground blobs Selective Temporal Feature Extraction Trained Event Models Detection Results
  • 11. The Proposed Method Frame-level features         Blob Area (BA) Filling Ratio (FR) Aspect Ratio (AR) Bounding Box Area (BBA) Bounding box Width (BBW) Bounding box Height (BBH) Blob Count (BC) Blob Distance (BD)
  • 12. The Proposed Method Temporal features 2 1 4 3 6 5 Frame #  Overlapping sliding window  Temporal order  Speed of variation
  • 13. The Proposed Method Blob Count (BC), Blob Area (BA)
  • 14. The Proposed Method Blob Distance (BD)
  • 15. The Proposed Method Aspect Ratio (AR)
  • 16. The Proposed Method Top five features for four different events Feature ranking using absolute value criteria of two sample t-test, based on pooled variance estimate.
  • 17. Experimental Results Specific Event Detection • • • • • • • Four different events: meet, split, runaway, and fight CAVIAR dataset with labelled frames 80% of the test frames for model training 100 iterations of 10-fold cross validation Remaining 20% of the test frames for testing SVM classifier as event models Separate model for each event
  • 18. Experimental Results
  • 19. Experimental Results Specific Event Detection Actual Predicted Severity
  • 20. Experimental Results Abnormal Event Detection • • • • University of Minnesota crowd dataset (UMN dataset) The Runaway event model No additional training or tuning Three different sites
  • 21. Experimental Results Abnormal Event Detection (UMN-9)
  • 22. Experimental Results Abnormal Event Detection (UMN-10)
  • 23. Experimental Results Abnormal Event Detection (UMN-01)
  • 24. Experimental Results Abnormal Event Detection (UMN-07)
  • 25. Experimental Results Performance Comparison Method AUC Our Method 0.89 Pure Optical Flow [1] 0.84 [1] R. Mehran, A. Oyama, and M. Shah, “Abnormal crowd behavior detection using social force model,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition CVPR 2009, 20–25 June 2009, pp. 935–942.
  • 26. Publication Mahfuzul Haque and Manzur Murshed, “Panic-driven Event Detection From Surveillance Video Stream without Track and Motion Features,” IEEE International Conference on Multimedia & Expo (ICME), 2010.
  • 27. Thanks! Q&A Mahfuzul.Haque@infotech.monash.edu.au