International Journal of Robotics Research
Gait Recognition by Applying Multiple Projections and Kernel PCA
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Multiple vehicle tracking in surveillance videos
CLEAR'06 Proceedings of the 1st international evaluation conference on Classification of events, activities and relationships
People detection by boosting features in nonlinear subspace
PCM'10 Proceedings of the 11th Pacific Rim conference on Advances in multimedia information processing: Part I
A survey of techniques for human detection in static images
Proceedings of the Second International Conference on Computational Science, Engineering and Information Technology
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In recent years Kernel Principal Component Analysis (Kernel PCA) has gained much attention because of its ability to capture nonlinear image features, which are particularly important for encoding image structure. Boosting has been established as a powerful learning algorithm that can be used for feature selection. In this paper we present a novel framework for object class detection that combines the feature reduction and feature selection abilities of Kernel PCA and AdaBoost respectively. The classifier obtained in this way is able to handle change in object appearance, illumination conditions, and surrounding clutter. A nonlinear subspace is learned for positive and negative object classes using Kernel PCA. Features are derived by projecting example images onto the learned subspaces. Base learners are modeled using Bayes classifier. AdaBoost is then employed to discover the features that are most relevant for the object detection task at hand. The proposed method has been successfully tested on wide range of object classes (cars, airplanes, pedestrians, motorcycles, etc) using standard data sets and has shown remarkable performance. Using a small training set, a classifier learned in this way was able to generalize the intra-class variation while still maintaining high detection rate. In most object categories we achieved detection rates of above 95% with minimal false alarm rates. We demonstrate the effectiveness of our approach in terms of absolute performance parameters and comparative performance against current state of the art approaches.