iSAM: Fast Incremental Smoothing and Mapping with Efficient Data Association

2007 ◽  
Author(s):  
Michael Kaess ◽  
Ananth Ranganathan ◽  
Frank Dellaert
Keyword(s):  
Data Association ◽  
Efficient Data ◽  
Smoothing And Mapping

Efficient data association to targets for tracking in passive wireless sensor networks

2018 ◽  
Vol 75-76 ◽  
pp. 19-32 ◽  
Author(s):  
Changhyuk An ◽  
Youngwon Kim An ◽  
Seong-Moo Yoo ◽  
B. Earl Wells
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Association ◽  
Wireless Sensor ◽  
Efficient Data

scholarly journals Automatic Classification Algorithm for Multisearch Data Association Rules in Wireless Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Cailing Li ◽  
Wenjun Li
Keyword(s):  
Wireless Network ◽  
Association Rules ◽  
Data Association ◽  
Automatic Classification ◽  
Classification Algorithm ◽  
Fuzzy Classification ◽  
Classification Error ◽  
Data Generation ◽  
High Coverage ◽  
Efficient Data

In order to realize efficient data processing in wireless network, this paper designs an automatic classification algorithm of multisearch data association rules in a wireless network. According to the algorithm, starting from the mining of multisearch data association rules, from the discretization of continuous attributes of multisearch data, generation of fuzzy classification rules, and the design of association rule classifier and other aspects, automatic classification is completed by using the mining results. Experimental results show that this algorithm has the advantages of small classification error, good real-time performance, high coverage rate, and high feasibility.

Efficient data association for move-stop-move target tracking

2008 ◽  
Author(s):  
T. Sathyan ◽  
Mike McDonald ◽  
T. Kirubarajan
Keyword(s):  
Target Tracking ◽  
Data Association ◽  
Efficient Data

Efficient Data Association for Multiple Vehicles Tracking

2012 ◽  
Vol 263-266 ◽  
pp. 2426-2431
Author(s):  
Seok Lyong Lee ◽  
Du Hyung Cho
Keyword(s):  
Tracking System ◽  
Data Association ◽  
Spatial Relationship ◽  
Region Of Interest ◽  
Experimental Result ◽  
Video Frames ◽  
Consecutive Frame ◽  
Multiple Vehicles ◽  
Efficient Data ◽  
Association Method

Data association problem has been an important issue for the multiple vehicles tracking in a vehicle tracking system. In this paper, we present an efficient data association method to track multiple vehicles in a sequence of traffic video frames. We first introduce the compact rectangular region-of-interest (crROI) that tightly encloses a vehicle and has the rotation-invariant property. The subsequent processing is based on the crROI instead of a vehicle image itself to avoid the processing overhead. Next, we extract the features from the crROI such as shape, size, and spatial relationship. Using these features, we define the similarity metric between two vehicles, and present the association method that matches a vehicle in a frame with the corresponding vehicle in its consecutive frame. An experimental result shows that the proposed method identifies and tracks vehicles effectively and efficiently in the curve or crossroad environment where multiple vehicles appear.

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