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This paper presents a method of learning global and reconfigurable part-based models (RPM) for object detection. Recently, deformable part-based model (DPM) is widely used. A DPM consists of a root node and a collection of part nodes, which is learned under the latent SVM formulation by treating part nodes as hidden variables. Although the configuration of parts (i.e., the shapes, sizes and locations...
The well-built dataset is a pre-requisite for object categorization. The processes of collecting and labeling the images are laborious and monotonous. In order to label images efficiently, we propose an incremental learning model to label images automatically with a bounding box for each visual object category. Our approach combines image classification and object detection. Given a query image, our...
Compared to the traditional tracking with fixed cameras, the PTZ-camera-based tracking is more challenging due to (i) lacking of reliable background modeling and subtraction; (ii) the appearance and scale of target changing suddenly and drastically. Tackling these problems, this paper proposes a novel tracking algorithm using patch-based object models and demonstrates its advantages with the PTZ-camera...
The problem of object tracking in dense clutter is a challenge in computer vision. This paper proposes a method for tracking object robustly by combining the online selection of discriminative color features and the offline selection of discriminative Haar features. Furthermore, the cascade particle filter which has four stages of importance sampling is used to fuse two kinds of features efficiently...
A camera can assist a robot to detect and track objects when it is used in a scout robot system, and hence, help the robot to navigate. This paper discusses problems of tracking a human face where an active camera is installed on a 2 DOF pan and tilt. Kinematics model between object in motion and image of camera is built. The face detection and tracking algorithm is introduced which uses complexion...
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