1. Object detection using image reconstruction with PCA
Source: Image and Vision Computing. March
2007.
Authors: Luis Malagon-Borja, Olac Fuentes.
Reporter: Yen-Chang, Chen.
Date: 2007/1/9.

2. Outline Introduction PCA classifier
Adding a Support Vector Machine classifier
Experimental results
Conclusions

3. Introduction The detection system SVM classifier Input image
Output≧0
=> Pedestrian
Output＜0
=> Non-pedestrian
Classifier based on Image
Reconstruction with PCA
SVM classifier
Reduction of false detections by means of heuristics
-Eliminating single detections
-Eliminating nearby detections
Input image
Output image

4. PCA classifier The principal component.PC (The eigenvectors of C) P
The first k eigenvectors of PC.
Mean object of the set
Covariance matrix C r
The reconstructed image p
The projection of the sub-image u.
Sub-image u. d
Reconstruction error

5. PCA classifier
Ex:
Ex:
The sets
g e
An image

6. Adding a Support Vector Machine classifier
Hyperplane M
Support vector
Support vector

7. Adding a Support Vector Machine classifier
Input image
PCA classifier
SVM classifier
(1)Group the detections
(2) Eliminating single detections
Eliminating single detections
Eliminating clustered detections
(1)Define a region
(2) Choice max Preference to keep

8. Experimental results
The capability of the system for detecting people in still images with cluttered backgrounds.

9. Experimental results
ROC curves comparing the performance of out classifiers versus the best reported in the literature.

10. Conclusions
Authors have presented an object detection system for static images.
This system is able to detect frontal and rear views of pedestrians, and usually it can also detect side views of pedestrians.