计算机与数字工程2018，Vol.46Issue(1)：16-20,5.DOI:10.3969/j.issn.1672-9722.2018.01.005
基于DeepMask和RJMCMC的遗留箱体检测
Abundant Box Detection Based on DeepMask and RJMCMC
摘要
Abstract
In this paper,the detection of legacy box in video surveillance is studied,and a detection scheme based on depth neural network feature extraction and segmentation and Bayesian network modeling is proposed. The depth neural network is used for feature extraction and individual segmentation to obtain the likelihood and box detection of the current frame,and the Bayesian modeling method is used to transform the tracking problem into the maximum posteriori estimation of the state,in the process of solv?ing the use of RJMCMC iterative sampling side,in order to achieve a variable multi-target tracking. And then by means of the RJM?CMC process of the three kinds of behavior in the"new"and tracking status,to determine whether the box is left,so as to achieve the video in the box detection. The quantitative analysis of the experimental results shows that the algorithm is effective.关键词
样本分割/可逆跳转马尔科夫链蒙特卡洛/贝叶斯推理/后验概率/多目标跟踪Key words
sample segmentation/reversible jumping Markov chain Monte Carlo/Bayesian reasoning/posterior probabili⁃ty/multi-target tracking分类
信息技术与安全科学引用本文复制引用
刘小虎,彭天亮,邢静..基于DeepMask和RJMCMC的遗留箱体检测[J].计算机与数字工程,2018,46(1):16-20,5.基金项目
陕西省教育厅专项科研计划项目(编号:16JK2140) （编号:16JK2140）
国家自然科学基金项目(编号:61701215) （编号:61701215）
江西省重点实验室开发基金项目(编号:2016WICSIP027)资助. （编号:2016WICSIP027）
