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

2. 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.

3. 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 ”

4. 2. 2 Partial occlusion handling
Translates the decision function of the whole linear SVM to a summation of classification results of each block

5. 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.

6. 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)

7. 3. 3 Evaluation by ourselves

8. Thanks