Feature Point-Based Object Tracking

Robust Feature-based Object Tracking

Object tracking is an important component of many computer vision systems. It is widely used in video surveillance, robotics, 3D image reconstruction, medical imaging, and human computer interface. In this paper, we focus on unsupervised object tracking, i.e., without prior knowledge about the object to be tracked. To address this problem, we take a feature-based approach, i.e., using feature p...

Multi-feature Graph-Based Object Tracking

We present an object detection and tracking algorithm that addresses the problem of multiple simultaneous targets tracking in realworld surveillance scenarios. The algorithm is based on color change detection and multi-feature graph matching. The change detector uses statistical information from each color channel to discriminate between foreground and background. Changes of global illumination...

Feature Based Object Tracking Using PTZ Camera

Object Recognition and Tracking based on Object Feature Extracting

Object recognition and tracking is one of the most important task in the field of computer vision and surveillance system. Among many proposed, object tracking using a feature matching method is popular and accurate, however, it has a room to improve on high computational complexity and weak robustness in various environments. This paper proposes a robust object recognition and tracking method,...

Characterizing the Uncertainties in Point Feature Motion for Model-based Object Tracking

We describe a model-based object tracking system that updates the connguration parameters of an object model based upon information gathered from a sequence of monocular images. We formulate the tracking problem as one of parameter estimation from partially observed data, and apply the Extended Kalman Filtering (EKF) algorithm. Kinematic object models and imaging models are used to determine wh...