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.

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

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

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

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

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

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

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

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

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.