Online Multiple Classifier Boosting as long as Object Tracking Learning Multi-Modal Representations Joint Clustering in addition to Training MCBoost (continued) MCBoost

Online Multiple Classifier Boosting as long as Object Tracking Learning Multi-Modal Representations Joint Clustering in addition to Training MCBoost (continued) MCBoost www.phwiki.com

Online Multiple Classifier Boosting as long as Object Tracking Learning Multi-Modal Representations Joint Clustering in addition to Training MCBoost (continued) MCBoost

Dixon, Patty, News Director has reference to this Academic Journal, PHwiki organized this Journal Online Multiple Classifier Boosting as long as Object Tracking Tae-Kyun Kim1 Thomas Woodley1 Björn Stenger2 Roberto Cipolla1 1Dept. of Engineering, University of Cambridge 2Computer Vision Group, Toshiba Research Europe The Task: Object Tracking Example sequence 1 Target appearance changes due to changes in pose illumination object de as long as mation Example sequence 2 Learning Multi-Modal Representations – Multi-view face detection [Rowley et al. 98, Schneiderman et al. 00, Jones Viola 03] – Multi-category detection, Sharing features [Torralba et al. 04] Positive examples Negative examples

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Joint Clustering in addition to Training K-means clustering Face cluster 1 Face cluster 2 Positive examples Negative examples [Kim in addition to Cipolla 08, Babenko et al. 08] Given: Set of n training samples with labels number of strong classifiers Learn strong classifiers: Combine classifier output with “Noisy OR” function Map to probabilities with sigmoid function MCBoost: Multiple Strong Classifier Boosting [Kim in addition to Cipolla 08, Babenko et al. 08] For given weights, find K weak-learners at t-th round of boosting to maximize Weak-learner weights found by a line search to maximize where Sample weight update by AnyBoost method [Mason et al. 00] MCBoost (continued)

MCBoost: Toy Example 1 Input data MCBoost result (K=3) Toy Example 2 St in addition to ard AdaBoost

MCBoost [Kim in addition to Cipolla 08] MC Boost with weighting function Q MC Boost with weighting function Q MCBQ Classifier Assignment Make classifier assignment explicit using function weight of strong classifier on sample is updated at each round of boosting. Here: K-component GMM in d-dim eigenspace, k-th mode is area of expertise of

Joint Boosting in addition to Clustering MCBoost MCBQ Input: Data set , set of weak learners Output: Strong classifiers as long as t=1, ,T // boosting rounds as long as k=1, ,K // strong classifiers Find weak learners in addition to their weights Update sample weights end end MCBQ Algorithm MCBQ as long as Object Tracking Principle: 1. (Short) supervised training phase 2. On-line updates

Online Boosting one sample Init importance Estimate errors Select best weak classifier Update weight Estimate importance Current strong classifier [Oza, Russel 01, Grabner, Bischof 06] Global classifier pool Estimate errors Select best weak classifier Update weight Estimate errors Select best weak classifier Update weight Estimate importance Online MCBQ Classifiers Sample weight distribution Selector Selector Selector Update Selector Selector Selector Select weak classifiers, add to Update weights, re-normalize Results

Improved Pose Expertise MCBoost MCBQ Multi-pose Tracking with MCBQ Tracking Experiments

Tracking “Cube” sequence MCBQ MILTrack SemiBoost Tracking Experiments Tracking error Summary Tracking: Build appearance model, then update online No detector is required, i.e. not object specific. H in addition to les rapid appearance changes. Simultaneous pose estimation in addition to tracking is possible. K is currently set by h in addition to . Incorrect adaptation may still occur. Extension of MCBoost to online setting Extension of MIL to multi-class

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Thank you Tracking: Generative vs Discriminative Generative Eigentracking [Black, Jepson 96] Appearance manifolds [Lee et al. 05] Discriminative Feature selection [Collins et al. 03] On-line boosting [Grabner et al. 06] AnyBoost related Multi-component boosting [Dollar et al ECCV08] MP boosting [Babenko et al ECCVW08] MCBoost [Kim in addition to Cipolla NIPS08] Noisy-OR boosting as long as multiple instance learning [Viola et al NIPS06]

Tracking Experiments Tracking error. Average center location errors rounded to nearest integer (in pixels). Algorithms compared are Semi-Boost [8] (best of 5 runs), MILTrack [3], our implementations of AdaBoost, MCBoost [13] in addition to MCBQ trackers. Bold font indicates best per as long as mance, italic second best. Cumulative errors are weighted by the number of frames per sequence. Updating Weighting Function Improvement by Online Updates Offline MCBQ on test set Online MCBQ on test set

Simultaneous Tracking in addition to Pose Estimation side view front view

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