Determining the location and scale of a particular object class in a 2D image is usually referred as object detection in the computer vision area. Object detection is a well studied topic and many successful algorithms have been proposed during the last two decades. However, recent experimental surveys reveal that the performance of the state-of-the-art detection systems still have low performance on images in unconstrained environment. The major reasons are due to high intra-class variation and object self-occlusion.;In this thesis, I will present a novel exemplar-based object detection framework that outperforms the state-of-the-art systems in terms of accuracy. The proposed method, Vector Array Recognition by Indexing and Sequencing (VARIS) (10), is designed to fulfill two requirements in object detection: Generalization and Reliability. The foundation of VARIS is to dynamically assemble an object exemplar that maximizes the similarity to the input image. Experimental results show that VARIS achieves better results than its competitors even with a very compact training dataset. Meanwhile, the computational speed is significantly increased with the help of a modified random forest module, which allows the full system to run in real time on standard images.;Beyond 2D object detection topic, I also explored the 3D computer vision domain. I designed and implemented a novel framework that estimated human body shape and pose simultaneously, which is named as parametric deformable model (PDM). PDM demonstrates the ability to recover the true human body pose and shape even by given a noisy and occluded 3D depth image as the input. PDM also brings many potential applications, such as better body joints estimation. Once the joint locations are determined, we can extend the 1D VARIS system to recognize the human activity.
展开▼
