Object detection using image reconstruction with PCA Source
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. 1
Outline • Introduction • PCA classifier • Adding a Support Vector Machine classifier • Experimental results • Conclusions 2
Introduction The detection syste Classifier based on Image Reconstruction with PCA Output≧ 0 => Pedestrian Output< 0 => Non-pedestrian SVM classifier Reduction of false detections by means of heuristics -Eliminating single detections -Eliminating nearby detections Input image Output image 3
PCA classifier Mean object of the set Sub-image u. Covariance matrix C The principal component. PC (The eigenvectors of C) p r The projection of the subimage u. The reconstruct ed image P The first k eigenvectors of PC. d Reconstruction error 4
PCA classifier Ex: The sets g e An image 5
Adding a Support Vector Machine classifier Support vector Hyperplane M Support vector 6
Adding a Support Vector Machine classifier Input image Eliminating single detections Eliminating clustered detections PCA classifier SVM classifier (1)Group the detections (2) Eliminating single detections (1)Define a region (2) Choice max Preference to keep 7
Experimental results The capability of the system for detecting people in still images with cluttered backgrounds. 8
Experimental results ROC curves comparing the performance of out classifiers versus the best reported in the literature. 9
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. 10
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