Fast Natural Feature Tracking Using Optical Flow
نویسندگان
چکیده
منابع مشابه
Using Optical Flow for Tracking
We present two observation models based on optical flow information to track objects using particle filter algorithms. Although, in principle, the optical flow information enables us to know the displacement of the objects present in a scene, it cannot be used directly to displace a model since flow estimation techniques lack the necessary precision. We will define instead two observation model...
متن کاملFast Natural Feature Tracking for Mobile Augmented Reality Applications
Fast natural feature tracking is essential to make markerless augmented reality applications practical on low performance mobile devices. To speed up the natural feature tracking process which includes computationally expensive procedures, we propose a novel fast tracking method using optical flow aimed for mobile augmented reality applications. Experimental results showed that the proposed met...
متن کاملRobust modified L2 local optical flow estimation and feature tracking
This paper describes a robust method for the local optical flow estimation and the KLT feature tracking performed on the GPU. Therefore we present an estimator based on the L norm with robust characteristics. In order to increase the robustness at discontinuities we propose a strategy to adapt the used region size. The GPU implementation of our approach achieves real-time (>25fps) performance f...
متن کاملPerformance Evaluation of Feature Detection for Local Optical Flow Tracking
Due to its high computational efficiency the Kanade Lucas Tomasi feature tracker is still widely accepted and a utilized method to compute sparse motion fields or trajectories in video sequences. This method is made up of a Good Feature To Track feature detection and a pyramidal Lucas Kanade feature tracking algorithm. It is well known that the Good Feature To Track takes into account the Apert...
متن کاملA Study of Feature Extraction Algorithms for Optical Flow Tracking
Sparse optical flow algorithms, such as the Lucas-Kanade approach, provide more robustness to noise than dense optical flow algorithms and are the preferred approach in many scenarios. Sparse optical flow algorithms estimate the displacement for a selected number of pixels in the image. These pixels can be chosen randomly. However, pixels in regions with more variance between the neighbors will...
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: The KIPS Transactions:PartB
سال: 2010
ISSN: 1598-284X
DOI: 10.3745/kipstb.2010.17b.5.345