.png){kind=icon}
返回
Sparse Representation-Based Semi-Supervised Regression for People Counting
.png){kind=icon}
PRE
AI
DOI：10.1145/3106156.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Label imbalance and the insufficiency of labeled training samples are major obstacles in most methods for counting people in images or videos. In this work, a sparse representation-based semi-supervised regression method is proposed to count people in images with limited data. The basic idea is to predict the unlabeled training data, select reliable samples to expand the labeled training set, and retrain the regression model. In the algorithm, the initial regression model, which is learned from the labeled training data, is used to predict the number of people in the unlabeled training dataset. Then, the unlabeled training samples are regarded as an over-complete dictionary. Each feature of the labeled training data can be expressed as a sparse linear approximation of the unlabeled data. In turn, the labels of the labeled training data can be estimated based on a sparse reconstruction in feature space. The label confidence in labeling an unlabeled sample is estimated by calculating the reconstruction error. The training set is updated by selecting unlabeled samples with minimal reconstruction errors, and the regression model is retrained on the new training set. A co-training style method is applied during the training process. The experimental results demonstrate that the proposed method has a low mean square error and mean absolute error compared with those of state-of-the-art people-counting benchmarks.
Keyword:
Counting people
sparse representation
semi-supervised regression
sparse reconstruction
reconstruction error
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6
论文数:
2.0K
被引数:
5.4K
机构
引用论文
Leveraging local neighborhood topology for large scale person re-identification.png)
PATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
分享收藏
查看更多内容