Moving object detection is one of the challenging problems in video monitoring systems, especially when the illumination changes and shadow exists. Amethod for real-time moving object detection is described. Anew back...Moving object detection is one of the challenging problems in video monitoring systems, especially when the illumination changes and shadow exists. Amethod for real-time moving object detection is described. Anew background model is proposed to handle the illumination varition problem. With optical flow technology and background subtraction, a moving object is extracted quickly and accurately. An effective shadow elimination algorithm based on color features is used to refine the moving obj ects. Experimental results demonstrate that the proposed method can update the background exactly and quickly along with the varition of illumination, and the shadow can be eliminated effectively. The proposed algorithm is a real-time one which the foundation for further object recognition and understanding of video mum＇toting systems.展开更多
To extract and tr ack moving objects is usually one of the most important tasks of intelligent video surveillance systems. This paper presents a fast and adaptive background subtraction alg...To extract and tr ack moving objects is usually one of the most important tasks of intelligent video surveillance systems. This paper presents a fast and adaptive background subtraction algorithm and the motion tracking process using this algorithm. The algorithm uses only luminance components of sampled image sequence pixels and models every pixel in a statistical model. The algorithm is characterized by its ability of real time detecting sudden lighting changes, and extracting and tracking motion objects faster. It is shown that our algorithm can be realized with lower time and space complexity and adjustable object detection error rate with comparison to other background subtraction algorithms. Making use of the algorithm, an indoor monitoring system is also worked out and the motion tracking process is presented in this paper. Experimental results testify the algorithm's good performances when used in an indoor monitoring system.展开更多
利用TE Model 49C型臭氧监测仪,于2004年1月1日—12月31日,在上甸子本底站进行了地面φ(O3)的连续在线监测.分析了全年φ(O3)的变化特征及其与同期气象要素的相关关系,并对φ(O3)高值日的个例分析进行了验证.结果表明,上甸子本底站地面...利用TE Model 49C型臭氧监测仪,于2004年1月1日—12月31日,在上甸子本底站进行了地面φ(O3)的连续在线监测.分析了全年φ(O3)的变化特征及其与同期气象要素的相关关系,并对φ(O3)高值日的个例分析进行了验证.结果表明,上甸子本底站地面φ(O3)具有明显的季节变化和日变化规律,并且与同期的气象条件密切相关.主要特征:①夏初φ(O3)较高,6月的平均值达到最高,小时平均最大值可达129.7μL/m3;而冬季φ(O3)较低,12月的平均值达到最低,小时平均最大值仅为32.7μL/m3.②日变化趋势较为明显,在4:00—7:00出现最低值,在15:00—18:00出现最高值,变化幅度为夏季最大、冬季较小.③气温与φ(O3)呈显著正相关,夏季相对湿度与φ(O3)呈显著负相关,风向和辐射强度也与φ(O3)及其变化规律呈显著相关关系.展开更多
基金This project was supported by the foundation of the Visual and Auditory Information Processing Laboratory of BeijingUniversity of China (0306) and the National Science Foundation of China (60374031).
文摘Moving object detection is one of the challenging problems in video monitoring systems, especially when the illumination changes and shadow exists. Amethod for real-time moving object detection is described. Anew background model is proposed to handle the illumination varition problem. With optical flow technology and background subtraction, a moving object is extracted quickly and accurately. An effective shadow elimination algorithm based on color features is used to refine the moving obj ects. Experimental results demonstrate that the proposed method can update the background exactly and quickly along with the varition of illumination, and the shadow can be eliminated effectively. The proposed algorithm is a real-time one which the foundation for further object recognition and understanding of video mum＇toting systems.
文摘To extract and tr ack moving objects is usually one of the most important tasks of intelligent video surveillance systems. This paper presents a fast and adaptive background subtraction algorithm and the motion tracking process using this algorithm. The algorithm uses only luminance components of sampled image sequence pixels and models every pixel in a statistical model. The algorithm is characterized by its ability of real time detecting sudden lighting changes, and extracting and tracking motion objects faster. It is shown that our algorithm can be realized with lower time and space complexity and adjustable object detection error rate with comparison to other background subtraction algorithms. Making use of the algorithm, an indoor monitoring system is also worked out and the motion tracking process is presented in this paper. Experimental results testify the algorithm's good performances when used in an indoor monitoring system.
文摘利用TE Model 49C型臭氧监测仪,于2004年1月1日—12月31日,在上甸子本底站进行了地面φ(O3)的连续在线监测.分析了全年φ(O3)的变化特征及其与同期气象要素的相关关系,并对φ(O3)高值日的个例分析进行了验证.结果表明,上甸子本底站地面φ(O3)具有明显的季节变化和日变化规律,并且与同期的气象条件密切相关.主要特征:①夏初φ(O3)较高,6月的平均值达到最高,小时平均最大值可达129.7μL/m3;而冬季φ(O3)较低,12月的平均值达到最低,小时平均最大值仅为32.7μL/m3.②日变化趋势较为明显,在4:00—7:00出现最低值,在15:00—18:00出现最高值,变化幅度为夏季最大、冬季较小.③气温与φ(O3)呈显著正相关,夏季相对湿度与φ(O3)呈显著负相关,风向和辐射强度也与φ(O3)及其变化规律呈显著相关关系.
