CAJ | 학술논문

背景估计是运动目标检测一项重要的前期工作，在城市交通等复杂场景中，存在大量慢速或暂停运动目标，背景模型很快受到污染，需要进行较多的后续处理或者采用高复杂度算法来检测前景。针对该问题，提出基于Sigma-Delta滤波改进的背景估计算法，融合可选择性背景更新机制和多频Sigma-Delta滤波背景估计方法，处理复杂场景中不同运动目标的运动特征，以获取稳定的背景。通过对典型城市路段和交叉口复杂交通场景序列进行对比实验，结果表明，该算法在保持Sigma-Delta滤波低内存消耗和高计算效率的基础上可获得更好的检测效果。
배경고계시운동목표검측일항중요적전기공작，재성시교통등복잡장경중，존재대량만속혹잠정운동목표，배경모형흔쾌수도오염，수요진행교다적후속처리혹자채용고복잡도산법래검측전경。침대해문제，제출기우Sigma-Delta려파개진적배경고계산법，융합가선택성배경경신궤제화다빈Sigma-Delta려파배경고계방법，처리복잡장경중불동운동목표적운동특정，이획취은정적배경。통과대전형성시로단화교차구복잡교통장경서렬진행대비실험，결과표명，해산법재보지Sigma-Delta려파저내존소모화고계산효솔적기출상가획득경호적검측효과。
Background estimation is an important preparatory work for moving object detection. In complex scenes,such as urban traffic,the background model is easily contaminated by a number of slow-moving or temporarily stopped moving object, and many subsequent processing steps or higher computational cost algorithms are needed to detect the foreground. To solve this problem,this paper proposes a background estimation algorithm based on the improved Sigma-Delta filtering, which is intended to achieve a more stable background model by combining a selective background updating mechanism with multiple-frequency Sigma-Delta background estimation method to deal with different object motion characteristics in complex scenes. The results of comparative experiment on complex traffic scenes sequences of typical urban road and intersection show that the proposed algorithm achieves better detection effects with keeping Sigma-Delta filtering high efficiency and low consumption performance.