Incremental Boosting Incremental Learning of Boosted Face Detector ICCV 2007 Unsupervised Incremental Learning for Improved Object Detection in a Video CVPR 2012 2014. 7. 1. Jeany Son

Outline Supervised incremental learning for boosted classifier Incremental Learning of Boosted Face Detector, ICCV 2007 Unsupervised incremental learning for boosted classifier Unsupervised Incremental Learning for Improved Object Detection in a Video, CVPR 2012

Incremental Learning for Boosted Face Detector ICCV 2007

Face Detection - Hard examples

Incremental Learning Offline Learning vs. Incremental Learning - +

Domain-partitioned Real Adaboost is minimized when where

Loss function for incremental learning Domain-partitioning Strong classifier Likelihood of incremental learning: Linear combination of offline & online parts Minimize upper bound on the training error by minimizing Z

Key issues 1) Adjustable parameters of the strong classifier H(x) 2) Estimation of Loff(H(x)) without offline samples 3) Choice of linear combination coefficient αy

Adjustable Parameters

F(x) is learned by means of some discriminative criterion e.g. KL divergence, Bhattacharyya distance Obtain proper domain partition of instance space for discrimination of different categories Small online samples  to adjust F(x) is not unreasonable Offline training : determine F(x) Incremental training : adjust G(z)

Estimating Offline Loss function (without offline samples) Naïve Bayes

Online Loss function & Optimization Minimize loss using Steepest-decent method

Linear Combination Coefficient ( α 𝑦 ) Online reinforcement ratio of category y Contributions of online/offline sample

Datasets False alarm that is learned incrementally

Comparisons in CMU+MIT frontal face dataset False alarm that is learned incrementally

Unsupervised Incremental Learning for Boosted Detector CVPR 2012

Unsupervised Incremental MIL Crowded environment and cluttered background

Unsupervised Incremental MIL Contribution MIL based incremental learning for Real Adaboost Unsupervised online sample collection

Unsupervised Incremental MIL Offline detector Tracker Online sample collection Incremental Learning Positive samples Negative samples Missing or Low confidence False alarm

Online Sample Collection Unmerged detection responses : detection responses obtained from all the scanning windows for a given video frame Merged detection responses : obtained using hierarchical clustering over all the unmerged detection responses Track these detection responses to obtain the tracks T l = T 1 , T 2 ,…, 𝑇 𝑚 [C.Huang, B.Wu, R.Nevatia, Robust object tracking by hierarchical association of detection responses, In ECCV 2008] Prune tracks which are less than ½ second or less than 10% of detection responses of track are confident

Online Sample Collection Positive Samples : missing or low confidence detection Negative Samples : False alarm Positive bag : consist of 10 patches around a missed detection Negative bag : one unmerged false alarm (30% overlap of detection with track response)

MIL loss function Soft max/min soft loss function

Overfitting Avoidance Inc1:base=offline detector Inc0:base=previous iteration

Detection results False alarm Missing object

Detection results False alarm Missing object