Efficient data association for robot 3D vision-SLAM

2010 ◽  
Author(s):  
Xiao-hua Wang ◽  
Dai-xian Zhu
Keyword(s):  
Data Association ◽  
3D Vision ◽  
Efficient Data

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.

scholarly journals Transforming multiple visual surveys of a natural environment into time-lapses

2019 ◽  
Vol 39 (1) ◽  
pp. 100-126
Author(s):  
Shane Griffith ◽  
Frank Dellaert ◽  
Cédric Pradalier
Keyword(s):  
Natural Environment ◽  
Data Association ◽  
Geometric Constraints ◽  
3D Vision ◽  
Formidable Challenge ◽  
Four Seasons ◽  
Flow Algorithm ◽  
Long Time ◽  
Image Pairs ◽  
New Framework

This article presents a new framework to help transform visual surveys of a natural environment into time-lapses. As data association across year-long variation in appearance continues to represent a formidable challenge, we present success with a map-centric approach, which builds on 3D vision for visual data association. We use a foundation of map point priors and geometric constraints within a dense correspondence image alignment optimization to align images and acquire loop closures between surveys. This framework produces many loop closures between sessions. Outlier loop closures are filtered in the frontend and in the backend to improve robustness. From the result map, the Reprojection Flow algorithm is applied to create time-lapses. The evaluation of our framework on the Symphony Lake Dataset, which has considerable variation in appearance, led to year-long time-lapses of many different scenes. In comparison with another approach based on using iterative closest point (ICP) plus a homography, our framework produced more and better-quality alignments. With many scenes of the 1.3 km environment consistently aligning well in random image pairs, we next produced 100 time-lapses across 37 surveys captured in a year. Approximately one-third had at least 20 (out of usually 33) well-aligned images, which spanned all four seasons. With promising results, we evaluated the pose error of misaligned image pairs and found that improving map consistency could lead to even better results.

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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