anomaly detection in surveillance

Download Anomaly Detection in Surveillance

Post on 25-May-2015




4 download

Embed Size (px)


Research in human gestures recognition


  • 1. Learning Abnormal Behavioural for Surveillance Systems By Ahmed Ibrahim 23/2/2011

2. Outline Introduction Key Challenges Proposed approaches Preliminary experiments Discussion and future directions 3. What is Abnormal Behaviour ? Abnormal Behaviour is a pattern in the data that does not conform to the expected normal behaviour. Also referred to as outliers, exceptions, suspicion, surprise, etc. Example: set of data points Feature space. F1 F2 N1 N2 o1 o2 O3 4. Video Surveillance Example A car on pedestrian roadway A piece of luggage left in a check in-area Source: Performance Evaluation of Tracking and Surveillance dataset (PETS) 5. Key Challenges Defining a representative normal behaviour is challenging. The boundary between normal and outlying behaviour is often not precise. The exact notion of an outlier is different for different video surveillance applications. Availability of labelled data for training/validation. Data always contain noise. Normal behaviour keeps evolving 6. Abnormal Behavior Detection Framework Motion Detection Background subtraction Temporal differencing Optical flow Object Tracking Model Region Active contour Feature based Behavior Understanding Classification (supervised) Clustering (unsupervised) Behavior Type Label Score Source: PETS 7. Unsupervised Behavior Modeling The following trajectory has been generating by: Applying principal component analysis on the video stream; Selecting the first three components Every point on the trajectory represents a frame from the video stream 8. Learning Outliers Behavior Model Gathering Real Data Statistically Resampling Visually Resampling 9. Proposed Statistical Approach 10. Statistical Resampling Example Subspace trajectory of waking pedestrian in outdoor PC: for principal components Irregular segments 11. Proposed Visual Approach 12. Visual Resampling Example Subspace trajectory of waking pedestrian Subspace trajectory of synthetic pedestrian Time delay Similar segmentsPC: for principal components 13. Behavior Model Output Label : each test instance is given a normal or anomaly label Score: each test instance is assigned an anomaly score Allows the output to be ranked Requires an additional threshold parameter 14. Research Plan To test the feasibility of statistical resampling: The Ionosphere dataset from UCI machine learning Repository will be used. This dataset are radar signals sent into the ionosphere and the class value indicates whether or not the signal returned information Good or Bad on the structure of the ionosphere. To test the feasibility of visual resampling: A set of animated videos with real backgrounds will be generated for the following events: Walk, Run, Jump, Gallop sideways, Bend , One-hand wave, Two-hands wave, Jump in place, Jumping Jack, Skip. 15. References [1] Weiming Hu; Tieniu Tan; Liang Wang; S. Maybank; , "A survey on visual surveillance of object motion and behaviors,"Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions, 2004 [2] Monekosso, Remagnino, Synthetic Training Data Generation for Activity Monitoring and Behavior Analysis , Proceedings of the European Conference on Ambient Intelligence, 2009. [3] Garg, Aggarwal, Sofat, Vision Based Hand Gesture Recognition, 2009. [4] Poppe, A survey on vision-based human action recognition, Image and Vision Computing Journal, 2010. [5] Mitra, Acharya, Gesture Recognition: A Survey, IEEE transactions on systems, man, and cybernetics, 2007. [6] Wang, Suter, Recognizing Human Activities from Silhouettes: Motion Subspace and Factorial Discriminative Graphical Model, IEEE Conference on In Computer Vision and Pattern Recognition, 2007. [7] Hua, Probabilistic Variational Methods for Vision based Complex Motion Analysis, PhD dissertation, Electrical and Computer Engineering, 2006. [8] Incertis, Garcia-Bermejo, Casanova, "Hand Gesture Recognition for Deaf People Interfacing", 18th International Conference on Pattern Recognition, 2006.