Online Object Tracking with Sparse Prototypes : The Studies
Its difficult to discuss studies that relate to Online Object Tracking with Sparse Prototypes.
Online Subspace Learning based Tracking Problem: A Robust Approach
A review about a robust online subspace learning-based tracking problem showed that sparse representation has been used in order to model each object observation as an individual task. This allowed for better reconstruction of the fading edges among objects.
Online Sparse Assemble for Object Tracking
An analysis about online sparse instance learning for object tracking was reported. This study used online multiple instance learning to improve the trackers performance over a reference tracker. The study found that using online sparse representation helped the tracker to overperform the reference WebTrackers on benchmark datasets.
Multitasking Sparse Prototype for TrackingHundreds of Objects
An analysis about using robust multitask sparse prototypes for tracking hundreds of objectsamples across multiple tasks is presented. The framework uses learning algorithms and a conceptually linked PCA subspace to passively rebuild forgotten relationships between observers. This improves the tracking capacity of models by allowing them to explore more complex interdependencies that may be unvisited by traditional methods.
Virtual Humans Tracking through Robust Multitask Prototypes
A study about the feasibility of effective tracking of virtual humans through robust multitask prototypes was conducted. A design for a robust multitask prototype was determined and results showed that it was possible to track the virtual humans successfully and accurately.
dynamic traffic Tracking: A Proven Strategy
A study about a proposed online multi-target tracking strategy for objects in dynamic traffic scenarios having severe occlusion was conducted. It was shown that the proposed approach works better than a traditional representation-based single tracking system and is more efficient.
Object Tracking and Detection in Gabor Filters: A Natural Way for Developers
A study about object tracking and detection in Gabor filters has been conducted. The results show that the frequency and orientation representations of Gabor filters are similar to those of the human brain. This study therefore provides a natural way for developers to create more complex object tracking and detection systems.
Sparse representation for visual tracking in open-source software
A study about efficient visual tracking via sparse representation has been conducted. The study found that sparse representation is a very efficient means for tracking objects since it does not require many extraround edges or local mesh will be needed.
Robust Multitasking Prototype Use for Visual Tracking
An evaluation about the use of robust multitask prototypes as a visual tracking tool was conducted. The study found that the usage of robust multitask prototypes is efficient and Effective to track visual information during a task. The trials showed that the prototype formulation supported efficient task performance with less errors than traditional design methods.
Citywide Traffic System: In the Loop
A research about a woman who is trying to cross a street in a city is going to need to take into account the fact that the traffic congestion in city areas can be very bad. An intelligent traffic system would be able to take this into account and provide the woman with detailed information about the current situation on the road and what are the best possible routes for her to take.
The online tracking of objects: A novel and effective algorithm
A study about an online tracking algorithm for robust visual tracking. In this study, they proposed a novel online object tracking algorithm that relies on the structural local appearance principle component analysis (PCA) algorithms. This has led to a novel and robust tracking algorithm that is both simple and effective.