Abundant Box Detection Based on DeepMask and RJMCMC
LIU Xiaohu
PENG Tianliang
XING Jing
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.
Keywords：sample segmentationreversible jumping Markov chain Monte CarloBayesian reasoningposterior probabili?tymulti-target tracking
Publication Date：2018-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：5( 16-20 )