Density-Based Multifeature Background Subtraction with Support Vector Machine

作者：Han Bohyung^*; Davis Larry S

来源：IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(5): 1017-1023.

DOI：10.1109/TPAMI.2011.243

• Background modeling and subtraction
• Haar-like features
• support vector machine
• kernel density approximation

Background modeling and subtraction is a natural technique for object detection in videos captured by a static camera, and also a critical preprocessing step in various high-level computer vision applications. However, there have not been many studies concerning useful features and binary segmentation algorithms for this problem. We propose a pixelwise background modeling and subtraction technique using multiple features, where generative and discriminative techniques are combined for classification. In our algorithm, color, gradient, and Haar-like features are integrated to handle spatio-temporal variations for each pixel. A pixelwise generative background model is obtained for each feature efficiently and effectively by Kernel Density Approximation (KDA). Background subtraction is performed in a discriminative manner using a Support Vector Machine (SVM) over background likelihood vectors for a set of features. The proposed algorithm is robust to shadow, illumination changes, spatial variations of background. We compare the performance of the algorithm with other density-based methods using several different feature combinations and modeling techniques, both quantitatively and qualitatively.

• 出版日期2012-5