摘要
准确的背景模型是目标提取与跟踪的重要基础。针对复杂场景中出现的局部拟周期变化的晃动目标,在多高斯背景模型基础上,提出一种拟周期背景算法(QPBA),用以抑制晃动目标,建立准确而稳定的背景模型。具体过程是:根据多高斯背景模型建立场景目标分类模型,分析晃动目标对高斯模型各参数产生的影响;以颜色分布值为样本建立高斯模型保留晃动所在像元,并以出现频次、时间间隔为权重因子,使晃动像元中的晃动模型融入背景模型。将拟周期背景算法与高斯混合模型(GMM)、背景建模算法(ViBe)、CodeBook等典型背景建模算法进行比较,通过定性、定量与效率三个方面的评估结果表明:拟周期背景算法对晃动目标抑制作用明显,误检率小于1%,可以很好地应对场景中晃动目标干扰;同时正检个数与其他算法保持一致,能够完整地保留运动目标;算法效率高,解算时间与CodeBook算法近似,满足实时性的计算要求。
Accurate background model is the paramount base for object extracting and tracing. In response to swing objects which part quasi-periodically changed in intricate scene, based on multi-Gaussian background model, a new QuasiPeriodic Background Algorithm( QPBA) was proposed to suppress the swing objects and establish an accurate and stable background model. The specific process included: According to multi-Gaussian background model, the object classification in scene was set up, and the effect on Gaussian model's parameters caused by swing objects was analyzed. By using color distribution values as samples to establish Gaussian model to keep swing pixels, the swing model in swing pixels was integrated into background model with weight factors of occurrence frequency and time interval. Comparison among QPBA and the classical background modeling algorithms such as GMM( Gaussian Mixture Model), ViBe( Visual Background extractor) and CodeBook was put forward, and the results were assessed in aspects of quality, quantity and efficiency. It shows that QPBA has a more obvious suppression on swing objects, and its fall-out ratio is less than 1%, so that it can handle the scene with swing objects. At the same time, its correct detection number is consistent with other algorithms, thus the moving objects can be reserved perfectly. In addition, the efficiency of QPBA is high, and its resolving time is approximate to CodeBook, which can satisfy the requirements of real-time computation.
出处
《计算机应用》
CSCD
北大核心
2014年第9期2691-2696,共6页
journal of Computer Applications
基金
国家自然科学基金资助项目(41161061
40901197)
关键词
晃动目标
高斯混合模型
拟周期
场景模型
抑制算法
swing object
Gaussian Mixture Model(GMM)
quasi-periodicity
scene model
restraining algorithm
