1. Ziming Zhang, Yucheng Zhao and Yiwen Wan

2.  Introduction&Motivation  Problem Statement  Paper Summeries  Discussion and Conclusions

3.  Anomaly is a pattern in the data that does not conform to the expected behaviour  Also referred to as outliers, exceptions, peculiarities, surprise, etc.

4. outlier  Vehicle behavior is represented as trajectories  When trajectory does conform to dominant pattern it is detected as anomaly or outlier  Collective Anomalies

5. Data Input: Spatio-temperal trajectories of moving objects

6. Scene Modeling: Scene Representation: interest points/path Learning Model:unsupervised/supervised

7. Activity Analysis: virtual fencing, speed profiling, path classification, anomaly detection, online activity analysis and object interaction characterization

8.  Accurate and efficient representation of trajectories  Defining a representative normal pattern is challenging  The boundary between normal and outlying behaviour is often not precise  Availability of labelled data for training/validation  Data might contain noise  Normal behaviour keeps evolving

9. Cláudio Rosito Jung, Member, IEEE, Luciano Hennemann, and Soraia Raupp Musse IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL. 18, NO. 11, NOVEMBER 2008

10. Representation Input Trajectories Initial Clustering Cluster Representation using 4-D histogram Event Detection (x1,y1) (x2,y2) (xn,yn) (x3,y3) …… F=(x1-x2,y1-y2,x2-x3,y2-y3…xn-xn-1,yn-yn-1)

11. Representation Input Trajectories Initial Clustering Cluster Representation using 4-D histogram Event Detection

12. Representation Input Trajectories Initial Clustering Cluster Representation using 4-D histogram Event Detection

13. Representation Input Trajectories Initial Clustering Cluster Representation using 4-D histogram Event Detection

14.  trajectories collected from trackers  Offline clustering based on Mixture of Gaussian is used for path modeling  4-D histogram is used to represent spatial and temporal characteristics of each cluster/path for further event detection such as drift, shift, entry, bifurcation, confluence, incoherent local speed, incoherent local orientation pattern  Two dataset(pedestrian and traffic scenario) are tested and 20 human observers were used for accuracy validation: the number of evaluation that agreed with results from proposed method

15. Nicolas Saunier and Tarek Sayed

16.  Reduction of public resources on detecting traffic collision.  Conflicting causes collisions  Conflicting definition › Two or more vehicles closed enough in time and space  Trajectory representation › A sequence of {x, y, v x, v y }

17.  HMM (Hidden Markov Model)

18. › Sequence of observation = {walk, shop, clean} › Compute the probability of observing a sequence, given a model. › Find the state sequence that maximizes the probability of the given sequence, when the model is known. (Viterbi) › Induce the HMM that maximizes the probability of the given sequence. (Baum- Welch)

19.  K-Means clustering

20.  HMM-based K-means clustering › A set of vehicle trajectories (sequences) › A set of initial HMM (k HMMs)  Step1: Calculating all the probabilities  Step2: Associating the trajectory with HMM that maximizes probability of the trajectory  Step3: Updating HMMs based on the temporary clustering result  Step4: Repeating step 1, 2 and 3 until convergence has been reached

21.  Training and testing the model › Several instances of conflicting trajectory pairs train the model to identify mutual conflicting trajectory clusters › New trajectories are associated with certain trajectory cluster based on the specific HMM probability maximization › Conflicting trajectories are identified by their clustering result.

22. C. Piciarelli *, G.L. Foresti Department of Mathematics and Computer Science, University of Udine, Via delle Scienze 206, 33100 Udine, Italy Available online 21 April 2006

23.  Problem: Classical two-step clustering algorithm can not update cluster dynamically  Solution: On-line trajectory clustering approach with a tree-like structure  Goal:Suit for video surveillances sysytems from image analysis to behavior analysis to detect anomalous events

24. Traditional trajectory clustering not suited for detect anomalous events  off-line: not useful in activity analysis  video system: complex structure,from moving ojects(low level) to behaviour analysis (high)

25. Representing trajectories as a tree of cluster  Trajectory(Ti): represented by a list of vectors Tij(representing a spatial position at time j)  Clusters(Ci): organized in a tree-like structure that, augmented with probability information, represented as a list of vectors  Define a distance or similarit to check if a Ti matches a given Ci( dynamically), when a Ti matches a Ci, cluster needs to be updated.

26. Tree creation steps: 1)building,create tree of clusters from acquried data dynamically,without waitting the end of trajectory. 2)maintenance as below:

27. For behaviour analysis, we define that an anomaly is an event happening rarely. Also we assume that dangerous events are generally anomalous. An anomalous trjectory can be defined as a trajectory matches a path in the tree with low probability. With probabilitic information, we can implement anomaly detection.

28. PapersLearning Fashion Path modeling Activity Analysis P1Offline unsupervised clustering and 4-D descriptor 7 abnormal events detection P2OfflineSemi-supervised HMM-Based clustering Conflicting traffic P3OnlineTree-structureanomaly detection

29.  Availability of labelled data for training/validation is not easy and unsupervised clustering is favored  online clustering is very important since normal behaviour keeps evolving  Approaches robust to noisy trajectories from tracking is preferred

30.  Questions?