Online sequential-extreme learning machine based detector on training-learning-detection framework

Maliha, Ayman; Yusof, Rubiyah; Madani, Ahmed

doi:10.1109/ascc.2015.7244867

Cited by 3 publications

(3 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Where H1 and H2 respectively represents the normalized histogram of the image to be detected and the positive and negative samples, N is the number of bin in the histogram, d is the correlation coefficient, the similarity range is [1,0].…”

Section: The Working Principle Of Slbp Classifiermentioning

confidence: 99%

See 1 more Smart Citation

A Novel Target Algorithm based on TLD Combining with SLBP

Zhang¹

2017

IJPE

View full text Add to dashboard Cite

TLD (Tracking-Learning-Detection) algorithm can be good for a long time to track the target in rotation, occlusion, illumination and other circumstances. However, in the case of uneven illumination, occlusion, tracking target fuzzy and so on, the problem of false tracking or tracking failure often occurs. Aiming at the shortcomings of TLD tracking algorithm, this paper will take TLD as the basic framework of target tracking and improve the detection module. When the tracking target has better texture feature, the SLBP (Semantic Local Binary Pattern) classifier is used to replace the nearest neighbor classifier in the detection module, which converts the image into SLBP texture feature vector to classify the samples using. In this paper, TLD-SLBP, MEEM, SCM, Struck and TLD are compared by using CVPR2013Benchmark test platform. The experiment results show that the TLD-SLBP algorithm has a higher success rate than other algorithms.

show abstract

Section: The Working Principle Of Slbp Classifiermentioning

confidence: 99%

“…After many years of research, it has made great progress. However, due to the complexity of the application of tracking tasks, the difficulty of target tracking is greatly increased [1]. Firstly, the pose, shape, texture and scale of the target are constantly changing.…”

Section: Introductionmentioning

confidence: 99%

A Novel Target Algorithm based on TLD Combining with SLBP

Zhang¹

2017

IJPE

View full text Add to dashboard Cite

show abstract

“…In the traditional extreme learning machine algorithm, when new data are obtained, the historical data will be repeatedly trained together with new data, which requires a lot of time. OS-ELM effectively avoids the repeated training of data and greatly improves the learning efficiency by using a partitioned matrix method [34][35][36].…”

Section: Os-elm Algorithmmentioning

confidence: 99%

Short-Term Load Forecasting Based on the Analysis of User Electricity Behavior

Guo

2016

Algorithms

View full text Add to dashboard Cite

Abstract:The smart meter is an important part of the smart grid, and in order to take full advantage of smart meter data, this paper mines the electricity behaviors of smart meter users to improve the accuracy of load forecasting. First, the typical day loads of users are calculated separately according to different date types (ordinary workdays, day before holidays, holidays). Second, the similarity between user electricity behaviors is mined and the user electricity loads are clustered to classify the users with similar behaviors into the same cluster. Finally, the load forecasting model based on the Online Sequential Extreme Learning Machine (OS-ELM) is applied to different clusters to conduct load forecasting and the load forecast is summed to obtain the system load. In order to prove the validity of the proposed method, we performed simulation experiments on the MATLAB platform using smart meter data from the Ireland electric power cooperation. The experimental results show that the proposed method is able to mine the user electricity behaviors deeply, improve the accuracy of load forecasting by the reasonable clustering of users, and reveal the relationship between forecasting accuracy and cluster numbers.

show abstract