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Max-Margin Early Event Detectors

Max-Margin Early Event Detectors. Minh Hoai Fernando De la Torre Robotics Institute, Carnegie Mellon University. Outline. Introduction Max-Margin Early Event Detectors Experiments. Introduction. Max-Margin Early Event Detectors (MMED )

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Max-Margin Early Event Detectors

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  1. Max-Margin Early Event Detectors Minh Hoai Fernando De la Torre Robotics Institute, Carnegie Mellon University

  2. Outline • Introduction • Max-Margin Early Event Detectors • Experiments

  3. Introduction • Max-Margin Early Event Detectors (MMED) • a novel formulation enabling early detection. • to detect the event as soon as possible, after it starts but before it ends

  4. Introduction • MMED inherits the advantages of SOSVM • convex learning formulation • ability for accurate localization of event boundaries • MMED is superior to SOSVM and other competing methods regarding the timeliness of the detection

  5. Introduction Example for SOSVM Pairs (x1, y1), ...., (xN, yN) :(image, (label, bounding box))

  6. Max-Margin Early Event Detectors • Learning with simulated sequential data Given training set : (X1, y1), … , (Xn, yn) Xi: time series, yi = [si, ei] Suppose the length of an event is bounded by lmin and lmax Output the segment that maximizes the detection score

  7. Max-Margin Early Event Detectors • Learning with simulated sequential data Linear detection score function φ(Xy) be the feature vector for segment Xy. θ = (w, b), w is the weight vector and b is the bias term

  8. Max-Margin Early Event Detectors • Learning with simulated sequential data Suppose the length of Xi is li, t = 1, …, li yitbe the part of event yi that has already happened From above, the desired property of the score function is:

  9. Max-Margin Early Event Detectors • Learning with simulated sequential data • Learning formulation

  10. Max-Margin Early Event Detectors • Loss function and empirical risk minimization Loss of the detector g Empirical risk

  11. Experiments • Evaluation criteria • Area under the ROC curve • AMOC curve • Normalized Time to Detection (NTtoD) : • F1-score curve

  12. Experiments • Synthetic data

  13. Experiments • Auslan dataset – Australian sign language

  14. Experiments • Extended Cohn-Kanade dataset – expression

  15. Experiments • Weizmann dataset – human action

  16. Conclusions • MMED is based on SOSVM, but extends it to anticipate sequential data • train a single event detector to recognize all partial events • can be applied to many domains

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