An HOG-LBP Human Detector with Partial Occlusion Handling Authors: Xiaoyu Wang, Xu Han and Shuicheng Yan Publication: ICCV09 (Oral) Reporter : Yazhou Liu Date : 22. 02.2010 *Note: some figures come from the authors’ presentation ppt: http://web.missouri.edu/~hantx/

1. The authors Wang Xiaoyu, Ph.D. Student, University of Missouri Xu Han, Assistant Professor University of Missouri, Director of Computer Vison Lab Yan Shuicheng, Assistant Professor Department of Electrical and Computer Engineering at National University of Singapore Director of the Learning and Vision Research Group.

2. Outline No occlusion handling: With occlusion handling: 2. 1 The concatenation of HOG feature and Cell-structured LBP Cell-structured LBP(8,1) … Why simple concatenation helps? Disadvantage of HOG: Focusing on edge, ignoring flat area Can not deal with noisy edge region Advantage of Cell-LBP: Treat all the patterns equally Filter out noisy patterns using the concept of “uniform patterns ”

2. 2 Partial occlusion handling Translates the decision function of the whole linear SVM to a summation of classification results of each block

2. 3 Distribute the constant bias to local classifiers positive training samples negative training samples the feature of the ith blocks of This approach of distributing the constant bias keeps the relative bias ratio across the whole training dataset.

3. Results 3. 1 Parameter test 3. 2 Evaluation INRIA (FPPW) Normalizationdifferent distance measurement L1-sqrt Norm: Bhattacharya distance, 3. 2 Evaluation INRIA (FPPW) INRIA (FPPI)

3. 3 Evaluation by ourselves

Thanks