CAJ | 학술논문

针对公路交通监控的需求增大与网络带宽资源有限的矛盾,本文提出一种贝叶斯压缩感知的目标检测算法.该算法采用小波基对信号进行稀疏,用部分哈达玛测量矩阵进行观测,实现视频的压缩,为了得到更为准确的前景,提出在部分时间均衡自适应背景模型下,将背景分割思想和小波树结构的贝叶斯压缩感知结合的重构算法完成目标检测.通过对多个场景监控视频的试验,验证了该方法的准确性和有效性,并对光线变化具有一定的鲁棒性和减少视频传输的成本.
침대공로교통감공적수구증대여망락대관자원유한적모순,본문제출일충패협사압축감지적목표검측산법.해산법채용소파기대신호진행희소,용부분합체마측량구진진행관측,실현시빈적압축,위료득도경위준학적전경,제출재부분시간균형자괄응배경모형하,장배경분할사상화소파수결구적패협사압축감지결합적중구산법완성목표검측.통과대다개장경감공시빈적시험,험증료해방법적준학성화유효성,병대광선변화구유일정적로봉성화감소시빈전수적성본.
@@@@To adapt the contradiction between the increasing demands of highway traffic network monitoring and the limited network bandwidth resources, an object detection algorithm is proposed based on Bayesian compressed sensing. Videos are sparse in a wavelet base,and the Hadamard measurement matrix is adopted to compress the videos. To get more accurate foreground, an adaptive background model of part time average mean is proposed. An object detection method is also proposed that combines background difference and Bayesian compressed sensing of wavelet tree structure. The proposed method can robustly detect the targets under changing light and reduce the price of video transmission. Through many experiments in video monitoring of multiple scenes,the results show the accuracy and effectiveness of the proposed method.