Robust Instruments Position Estimation using Improved Kernelized Correlation Filter for Substation Patrol Robots

2019 ◽  
Vol 33 (13) ◽  
pp. 1954033
Author(s):  
Jianying Yuan ◽  
Jiajia Liu
Keyword(s):  
Position Estimation ◽  
Candidate Region ◽  
Target Localization ◽  
Correlation Filter ◽  
Determination Method ◽  
Safe Operation ◽  
Precise Position ◽  
Point Determination ◽  
Target States ◽  
Candidate Regions

Substation patrol robots (SIR) play an increasingly important role in ensuring the safe operation of substations. The robust and precise position estimating of the instruments to be inspected on the images are a prerequisite for accurately detecting the target states or obtaining the target readings under all-weather environment. In order to achieve high location accuracy of instrument, this study proposed an improved kernelized correlation filter (KCF) algorithm for achieving robust instrument location on images for SPR. Firstly, multiple templates are selected for training KCF classifier parameters. Then, reliable SURF matching-point determination method is designed, and the regions including reliable matching points are selected as the candidate regions, so that the searching range is narrowed. Finally, for KCF response surface of each candidate region, Single-Peak Constraint (SPC) is designed for locating target and reducing mismatching rate. Furthermore, experiments are performed for validating the effectiveness of the proposed algorithm, in which four instruments mainly including lightning arrester monitor and transformer thermometer are selected. The experimental results show that the proposed method has higher accuracy of target localization than traditional SURF-based position estimating method.

Training Back-Propagation Neural Network for Target Localization Using Improved Particle Swarm Optimization

2013 ◽  
Vol 333-335 ◽  
pp. 1384-1387
Author(s):  
Jin Jie Yao ◽  
Xiang Ju ◽  
Li Ming Wang ◽  
Jin Xiao Pan ◽  
Yan Han
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Back Propagation ◽  
Particle Swarm ◽  
Back Propagation Neural Network ◽  
Position Estimation ◽  
Target Localization ◽  
Prediction Ability ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization

Target localization technology has been intensively studied and broadly applied in many fields. This paper presents one improved particle swarm optimization technique in training a back-propagation neural network for position estimation in target localization. The proposed scheme combines particle swarm optimization (PSO), back-propagation neural network (BP), adaptive inertia weight and hybrid mutation, called IPSO-BP. To verify the proposed IPSO-BP approach, comparisons between the PSO-based BP approach (PSO-BP) and general back-propagation neural network (BP) are made. The computational results show that the proposed IPSO-BP approach exhibits much better performance in the training process and better prediction ability in the validation process than those using the other two base line approaches.

Fast motion tracking based on moth-flame optimization and kernel correlation filter

2020 ◽  
Vol 39 (3) ◽  
pp. 3825-3837
Author(s):  
Yibin Chen ◽  
Guohao Nie ◽  
Huanlong Zhang ◽  
Yuxing Feng ◽  
Guanglu Yang
Keyword(s):  
Motion Tracking ◽  
State Of The Art ◽  
Candidate Region ◽  
Correlation Filter ◽  
Data Sets ◽  
Search Window ◽  
Tracking Method ◽  
Fast Motion ◽  
Kernel Correlation

Kernel Correlation Filter (KCF) tracker has shown great potential on precision, robustness and efficiency. However, the candidate region used to train the correlation filter is fixed, so tracking is difficult when the target escapes from the search window due to fast motion. In this paper, an improved KCF is put forward for long-term tracking. At first, the moth-flame optimization (MFO) algorithm is introduced into tracking to search for lost target. Then, the candidate sample strategy of KCF tracking method is adjusted by MFO algorithm to make it has the capability of fast motion tracking. Finally, we use the conservative learning correlation filter to judge the moving state of the target, and combine the improved KCF tracker to form a unified tracking framework. The proposed algorithm is tested on a self-made dataset benchmark. Moreover, our method obtains scores for both the distance precision plot (0.891 and 0.842) and overlap success plots (0.631 and 0.601) on the OTB-2013 and OTB-2015 data sets, respectively. The results demonstrate the feasibility and effectiveness compared with the state-of-the-art methods, especially in dealing with fast or uncertain motion.

scholarly journals Precise Vehicle Position and Heading Estimation Using a Binary Road Marking Map

2019 ◽  
Vol 2019 ◽  
pp. 1-18 ◽  
Author(s):  
Kyu-Won Kim ◽  
Jun-Hyuck Im ◽  
Moon-Beom Heo ◽  
Gyu-In Jee
Keyword(s):  
Autonomous Vehicles ◽  
Estimation Algorithm ◽  
Position Estimation ◽  
Precise Position ◽  
Vehicle Localization ◽  
The Road ◽  
Grid Map ◽  
Rms Error ◽  
Heading Estimation ◽  
Road Marking

Road markings are always present on roads to guide and control traffic. Therefore, they can be used at any time for vehicle localization. Moreover, they can be easily extracted by using light detection and ranging (LIDAR) intensity because they are brightly colored. We propose a vehicle localization method using a 2D road marking grid map. The grid map inserts the map information into the grid directly. Thus, an additional process (such as line detection) is not required and there is no problem due to false detection. We obtained road marking using a 3D LIDAR (Velodyne HDL-32E) and binarized this information to store in the map. Thus, we could reduce the map size significantly. In the previous research, the road marking grid map was used only for position estimation. However, we propose a position-and-heading estimation algorithm using the binary road marking grid map. Accordingly, we derive more precise position estimation results. Moreover, position reliability is an important factor for vehicle localization. Autonomous vehicles may cause accidents if they cannot maintain their lane momentarily. Therefore, we propose an algorithm for evaluating map matching results. Consequently, we can use only reliable matching results and increase position reliability. The experiment was conducted in Gangnam, Seoul, where GPS error occurs largely. In the experimental results, the lateral root mean square (RMS) error was 0.05 m and longitudinal RMS error was 0.08 m. Further, we obtained a position error of less than 50 cm in both lateral and longitudinal directions with a 99% confidence level.

A Tracking Window Adaptive Compressive Tracking Algorithm

2019 ◽  
Vol 33 (14) ◽  
pp. 1959045 ◽  
Author(s):  
Wenhao Wang ◽  
Mingxin Jiang ◽  
Xiaobing Chen ◽  
Li Hua ◽  
Shangbing Gao
Keyword(s):  
Texture Features ◽  
Candidate Region ◽  
Tracking Algorithm ◽  
Experimental Results ◽  
Scale Invariant ◽  
Compressive Tracking ◽  
Multi Scale ◽  
Feature Normalization ◽  
Object Region ◽  
Candidate Regions

In the original compression tracking algorithm, the size of the tracking box is fixed. There should be better tracking results for scale-invariant objects, but worse tracking results for scale-variant objects. To overcome this defect, a scale-adaptive compressive tracking (CT) algorithm is proposed. First of all, the imbalance of the gray and texture features in the original CT algorithm is balanced by the multi-feature method, which makes the algorithm more robust. Then, searching different candidate regions by using the method of multi-scale search along with feature normalization makes the features extracted from different scales comparable. Finally, the candidate region with the maximum discriminate degree is selected as the object region. Thus, the tracking-box size is adaptive. The experimental results show that when the object scale changes, the improving CT algorithm has higher accuracy and robustness than the original CT algorithm.

scholarly journals Exploring the 7p22.1 Chromosome as a Candidate Region for Autism

2010 ◽  
Vol 2010 ◽  
pp. 1-4 ◽  
Author(s):  
Nadia Bayou ◽  
Ahlem Belhadj ◽  
Hussein Daoud ◽  
Sylvain Briault ◽  
M. Bechir Helayem ◽  
...  
Keyword(s):  
Chromosomal Aberrations ◽  
De Novo ◽  
Position Effect ◽  
Candidate Region ◽  
Adult Brain ◽  
Blood Levels ◽  
Positional Candidate ◽  
Chromosomal Breakpoints ◽  
Candidate Regions

A high incidence of de novo chromosomal aberrations in a population of persons with autism suggests a causal relationship between certain chromosomal aberrations and the occurrence of autism. A previous study on a Tunisian boy carrying a t(7;16) translocation identified the 7p22.1 as a positional candidate region for autism on chromosome 7. The characterization of the chromosomal breakpoints helped us to identify new candidate regions on chromosome 16p11.2 which contain no known genes and the other one on 7p22.1 containing a portion of genes (NP 976327.1, RBAK, Q6NUR6 also called RNF216L and MMD2). We proposed Q6NUR6 (RNF216L) as a candidate gene for autism due to its vicinity to the translocation breakpoint on the chromosome derivative 7. Q6NUR6 is predicted to be an E3ubiquitin-ligase. Quantitative PCR demonstrates that Q6NUR6 gene has an ubiquitous expression and that it is strongly expressed in fetal and adult brain. The Q6NUR6 expression is increased in the patient blood cells in comparison to controls. This is the first report of Q6NUR6 gene (E3 ubiquitin ligase TRIAD3 EC 6.3.2) increasing blood levels in a patient with autism. It's probably caused by a position effect involving this gene and modifying its expression.

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