CAJ | 학술논문

本文提出一种基于目标检测的多维假设多目标检测和跟踪方法,此算法对于序列图像的照明变化和遮蔽现象具有很高的鲁棒性.首先,对序列图像进行背景抑制、时域滤波和杂波剔除的预处理,得到单帧的初始目标检测结果.对于初始检测中存在的漏检和误检现象,采用基于假设理论的跟踪概率模型优化初始检测轨迹;将目标的跟踪信息反馈于目标检测模块,形成一闭环自适应跟踪系统,达到多目标的最优检测和跟踪.实验结果表明了所提出的方法在多目标检测和跟踪中的可行性和有效性.
본문제출일충기우목표검측적다유가설다목표검측화근종방법,차산법대우서렬도상적조명변화화차폐현상구유흔고적로봉성.수선,대서렬도상진행배경억제、시역려파화잡파척제적예처리,득도단정적초시목표검측결과.대우초시검측중존재적루검화오검현상,채용기우가설이론적근종개솔모형우화초시검측궤적;장목표적근종신식반궤우목표검측모괴,형성일폐배자괄응근종계통,체도다목표적최우검측화근종.실험결과표명료소제출적방법재다목표검측화근종중적가행성화유효성.
A multiple hypothesis method for tracking multiple motion objects is proposed based on the object detection, which is robust to changes illumination and shadow conditions in image sequence. After video preprocessing including background suppression, temporal filter and clutter removal, the initial results of object detection is obtained in each frame image. Based on the detection results which may have missing and/or false detection, the multiple object tracking algorithm accepts the probabilities of initial object detection and keeps multiple hypotheses of object in a graph structure. Through extending and pruning the graph based on the previous image information, the multiple object tracking method gives feedbacks which are predictions of object locations to the object detection module. Therefore, the method integrates object detection and tracking tightly. The most possible hypothesis provides the multiple motion object tracking results. The experiment and evaluation results illustrate the validity and advantage of the proposed algorithm.