Real-Time Tracking with Online Constrained Compressive Learning
نویسندگان
چکیده
منابع مشابه
Real-Time Compressive Tracking
It is a challenging task to develop effective and efficient appearance models for robust object tracking due to factors such as pose variation, illumination change, occlusion, and motion blur. Existing online tracking algorithms often update models with samples from observations in recent frames. While much success has been demonstrated, numerous issues remain to be addressed. First, while thes...
متن کاملReal-Time Multi-Person Tracking with Time-Constrained Detection
This paper presents a robust real-time multi-person tracking framework for busy street scenes. Tracking-by-detection approaches have recently been successfully applied to this task. However, their run-time is still limited by the computationally expensive object detection component. In this paper, we therefore consider the problem of making best use of an object detector with a fixed and very s...
متن کاملOnline Learning of Linear Predictors for Real-Time Tracking
Although fast and reliable, real-time template tracking using linear predictors requires a long training time. The lack of the ability to learn new templates online prevents their use in applications that require fast learning. This especially holds for applications where the scene is not known a priori and multiple templates have to be added online. So far, linear predictors had to be either l...
متن کاملReal-Time Tracking Framework with Adaptive Features and Constrained Labels
This paper proposes a novel tracking framework with adaptive features and constrained labels (AFCL) to handle illumination variation, occlusion and appearance changes caused by the variation of positions. The novel ensemble classifier, including the Forward-Backward error and the location constraint is applied, to get the precise coordinates of the promising bounding boxes. The Forward-Backward...
متن کاملReal-time Tracking Meets Online Grasp Planning
This paper describes a synergistic integration of a grasping simulator and a real-time visual tracking system, that work in concert to 1) find an object’s pose, 2) plan grasps and movement trajectories, and 3) visually monitor task execution. Starting with a CAD model of an object to be grasped, the system can find the object’s pose through vision which then synchronizes the state of the robot ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEICE Transactions on Information and Systems
سال: 2013
ISSN: 0916-8532,1745-1361
DOI: 10.1587/transinf.e96.d.988