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Semelhante a Talk 2010-monash-seminar-panic-driven-event-detection
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Talk 2010-monash-seminar-panic-driven-event-detection
- 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
- 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.