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Fuzzy Systems Lifelog management

Case Study 2 A hierarchical Bayesian network for event recognition of human actions and interactions Sangho Park, J.K. Aggarwal Multimedia Systems, vol. 10, pp. 164-179, 2004. Fuzzy Systems Lifelog management. Outline. Overview Post estimation using a hierarchical Bayesian network

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Fuzzy Systems Lifelog management

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  1. Case Study 2A hierarchical Bayesian network for event recognitionof human actions and interactionsSangho Park, J.K. AggarwalMultimedia Systems, vol. 10, pp. 164-179, 2004 Fuzzy SystemsLifelog management

  2. Outline • Overview • Post estimation using a hierarchical Bayesian network • Recognition by DBN • Relative constraints • Experiment • Summary

  3. Overview • Recognition of human interaction • Applications: video surveillance, video-event annotation, virtual reality, human-computer interaction, and robotics • Difficulty: Ambiguity caused by body articulation, loose clothing, and mutual occlusion between body parts • Previous work • A method to segment and track multiple body parts in two-person interactions • Multilevel processing at pixel level, blob level, and object level

  4. Motivation • A methodology • To estimate body-part pose • To recognize different two-person interactions including pointing, punching, standing hand-in-hand, pushing, and hugging • System component • Bayesian network: estimate body poses • Dynamic Bayesian network: classify a sequence of body poses

  5. Head Pose Estimation • Environmental setup: lighting conditions, reflectance of light from the head • Head pose: head’s 3D rotation angles, visible part

  6. Head Pose Estimation: Example • V1: angle of the vector • V2: ratio of the two ellipses • a, B: fixed • P(V1=C|H2=B) = 0.18

  7. Arm Pose Estimation

  8. Arm Pose Estimation: Example • P(V5=B|H3=C, H4) = 0.34

  9. Leg Pose Estimation

  10. Leg Pose Estimation: Example

  11. Overall Body Pose Estimation (1)

  12. Overall Body Pose Estimation (2)

  13. Body-part Pose Recognition by DBN • DBN hidden states • Q1: set of DBNs for legs {“both legs are together on the ground”, “both legs are spread on the ground”, “and “one foot is moving in the air while the other is on the ground”} • Q2: set of DBNs for the torso {“stationary”, “moving forward”, and “at least one arm gets withdrawn”} • Q3: set of DBNs for arms {“both arms stay down”, “at least one arm stretches out”, and “at least one arm gets withdrawn”}

  14. Interaction Recognition • Whole-body pose: q1, q2, q3 • {“stand still with arms down”, “move forward with arm(s) stretched outward”, “move backward with arm(s) raised up”, “stand stationary while kicking with leg(s) raised up”, etc.} • Two-person interaction • Subject = {torso, arm, leg} • Verb = {raise, lower, stretch, withdraw, stay, move forward, move backward} • Object = {head, upper body, hand, lower body}

  15. Relative Constraints: Spatial • Examples • “standing hand-in-hand”: the torsos of the two persons be side by side and facing in the same direction • “pointing at the opposite person”: the torsos of face one another • Relative position and orientation • Gross level: proximity between two persons • Intermediate level: relative orientations of the torso poses between the two persons • Detailed level: relative configuration of individual body parts

  16. Relative Constraints: Temporal • Interval temporal logic: before, meet, overlap, start, during, and finish • Example • A pushing interaction • Event A: a person moving forward with arms stretched outward toward the second person • Event B: m of the second person

  17. Experimental Results • Human interaction (9):approaching, departing, pointing, standing hand-in-hand, shaking hands, hugging, punching, kicking, and pushing • Image • 320*240 pixels • 15 fps • 6 pairs of different people with various clothing • 56(?) sequences (6*9)

  18. Interaction Examples

  19. BN’s Belief Changes

  20. Performance of DBNs • Leave-one-out-cross-validation • Training: 5 sequences, test: 1 sequence • Accuracy: 78% • Approaching: 100 • Departing: 100 • Pointing: 67  similar interaction • Standing hand-in-hand: 83 • Shaking hands: 100 • Hugging: 50  occlusion • Punching: 67  similar interaction • Kicking: 83 • Pushing: 50  similar interaction

  21. Semantic Interpretation

  22. Summary • Contribution • A hierarchical framework for the recognition of two-person interactions • BN for managing ambiguity in human interaction • A human-friendly vocabulary for high-level event description • Stochastic graphical model • Future works • Extending the method to crowd behavior recognition • Incorporating various camera-view points • Recognizing more diverse interaction patterns

  23. Case Study 3Evolutionary Learning of Dynamic Probabilistic Models with Large Time LagsA. Tucker, X. Liu and A. Ogden-SwiftInternational Journal of Intelligent Systems,Vol. 16, no. 5, pp.621-646, 2001. Fuzzy SystemsLifelog management

  24. Outline • Introduction • Background • Methodology • Algorithm • Evaluation • Conclusions

  25. Introduction • Multivariate time-series (MTS) • A large number of interdependent variables • Large time lags between causes and effects (ex. Oil refinery processing) • Learning dynamic Bayesian networks • Not focused on learning models automatically • Focused on models with small time lags • Challenge task for large datasets with large possible time lags

  26. Background Dynamic Bayesian Networks • Bayesian networks • A set of n nodes {x1,…, xn}, representing the N variables in the domain • Each node, xi has a finite set of ri mutually exclusive states, vi1 to viri. • Each node xi with a set of parents, πi has an associated probability table P(xi|πi). • Dynamic Bayesian networks consist of BNs at differing time slices • Links over different time lags (non-contemporaneous links) and within the same time lag (contemporaneous links)

  27. Background Learning Bayesian Network Structures • K2/K3 algorithms • Use a greedy search which begins with an empty structure with no links • Explores the effect of adding each of the possible links to the current structure • K2 (a log likelihood metric) / K3 (a description length metric) • Branch and Bound technique • Perform an efficient exhaustive search by stopping any further exploration along a search path based on a bound • Evolutionary methods • Larranaga et al. used a genetic algorithm with a repair operator to remove cycles • Wong et al. used evolutionary programming with freeze, defrost and a knowledge guided mutation (KGM) • Sahami used the mutual information • Missing data management: Structural EM algorithm with Dempster’s expectation maximization algorithm

  28. Background Evolutionary Learning Bayesian Network Structures • Learning BNs involves scoring candidate network structures • Log likelihood • Description length metric of a network structure • Description length metric of encoding the dataset given that model • n: number of nodes • ri: possible instantiations of the node • qi: possible instantiations of the parent nodes • Fij = ∑Fijk • Fijk: frequency of occurrences in the dataset that the node xi takes on the value vikand the parent nodes πi take on the instantiation wij

  29. Methodology Representation • Assume that a dynamic network contains no contemporaneous links • n = N (# of variables at a single time slice) + Q (# of variables at previous time slice) • A list of triples represents a possible networks (a,b,l) • a: the parent variable • b: the child variable • l: the time lag

  30. Methodology Useful Heuristics • No contemporaneous links • Finding a good network structure  finding a group of simple tree structures • LagMutation: Each mutation is based on a uniform distribution with mean equal to the present lag • Autoregression links (a,a,1)

  31. Methodology Seeded GA for Search • Seed the entire first population with links found from the single link analysis • Using an approximate method to find a good list of single links rather than scoring the entire set • Exploiting this knowledge in the first population by seeding it entirely with a random selection of good links • EP method is particularly efficient at finding a good selection of links with good correlation • An individual represents a single triples • Self-adapting parameters (SAP)

  32. Algorithm

  33. Evaluation: Efficiency (1) • Adapting static BN search algorithms for DBN search • K2/K3 • The genetic algorithm • The evolutionary program • Knowledge guided mutation (KGM)

  34. Evaluation: Efficiency (2)

  35. Evaluation: Structural Comparison

  36. Conclusion • Problem: Learning dynamic probabilistic models with large time lags • Proposed method: EP-Seeded GA • Future works: Discretisation & parameterisation • Brainstorming • Mutually not-exclusive states  Fuzzy BN • Hybrid of the GA and K2/K3

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