Abstract:
In the digital video signal processing, the technology of video moving object detection is very important. Mining meaning moving objects from a video image sequence by a certain method is its intention. In this paper, according to the analysis of the deficiency of the traditional threshold method, an improved 2D gray-level histogram combined with genetic algorithm is applied to enhance the speed and efficiency. A new method based on adaptive background subtraction and frame difference for the real-time detection of moving cars in complex scenes is proposed. Experimental results show that the new method has better environmental adaptive ability and can detect moving cars well.
Key words:
thresholding,
genetic algorithm,
frame difference,
background compensation
摘要: 视频运动目标检测是数字视频处理、分析应用的一个重要领域,其目的是把作为一个整体的视频图像序列,通过一定的方法挖掘出具有意义的运动实体数据。该文对传统阈值法的缺陷进行分析,采用改进的二维阈值结合遗传的方法提高求解寻优的速度和效率,并通过帧差结合背景补偿的方式,提出一种适合于在复杂背景环境下实时检测运动车辆的新方法。实验结果表明,该方法有较强的环境适应能力,能够很好地检测出运动车辆。
关键词:
阈值化,
遗传算法,
帧差,
背景补偿
CLC Number:
ZHUANG Wei-wei; JIANG Qing-shan; HONG Zhi-ling. Improwed Detection Technology of Video Cars in Complex Scenes[J]. Computer Engineering, 2008, 34(16): 221-223.
庄蔚蔚;姜青山 ;洪志令. 一种改进的复杂背景下视频车辆检测技术[J]. 计算机工程, 2008, 34(16): 221-223.