Learning Robust Features for Planar Object Tracking
نویسندگان
چکیده
منابع مشابه
A planar object tracking approach robust to total occlusion
This paper presents a novel algorithm for performing planar object detection in partial/total occlusion situations. Firstly, all scene features are computed and matched against the reference template ones. Secondly, a test identifies if the behavior of the features surrounding the template is similar to the reference template and then the reference feature database is updated. Experiments on bo...
متن کاملRobust Object Tracking Based on Tracking-Learning-Detection
Current state-of-the-art methods for object tracking perform adaptive tracking-by-detection, meaning that a detector predicts the position of an object and adapts its parameters to the object’s appearance at the same time. While suitable for cases when the object does not disappear from the scene, these methods tend to fail on occlusions. In this work, we build on a novel approach called Tracki...
متن کاملConvolutional Gating Network for Object Tracking
Object tracking through multiple cameras is a popular research topic in security and surveillance systems especially when human objects are the target. However, occlusion is one of the challenging problems for the tracking process. This paper proposes a multiple-camera-based cooperative tracking method to overcome the occlusion problem. The paper presents a new model for combining convolutiona...
متن کاملLearning online structural appearance model for robust object tracking
The main challenge of robust object tracking comes from the difficulty in designing an adaptive appearance model that is able to accommodate appearance variations. Existing tracking algorithms often perform self-updating of the appearance model with examples from recent tracking results to account for appearance changes. However, slight inaccuracy of tracking results can degrade the appearance ...
متن کاملLearning Object Intrinsic Structure for Robust Visual Tracking
In this paper, a novel method to learn the intrinsic object structure for robust visual tracking is proposed. The basic assumption is that the parameterized object state lies on a low dimensional manifold and can be learned from training data. Based on this assumption, firstly we derived the dimensionality reduction and density estimation algorithm for unsupervised learning of object intrinsic ...
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: IEEE Access
سال: 2019
ISSN: 2169-3536
DOI: 10.1109/access.2019.2924732