An Improved Real-Time Blob Detection for Visual Surveillance

Blob detection is an essential ingredient process in some computer applications such as intelligent visual surveillance. However, previous blob detection algorithms are still computationally heavy so that supporting real-time multichannel intelligent visual surveillance in a workstation or even one-channel real-time visual surveillance in an embedded system using those turns out prohibitively difficult. Blob detection in visual surveillance goes through several processing steps: foreground mask extraction, foreground mask correction, and blob segmentation through connected component labeling. Foreground mask correction necessary for a precise detection is usually accomplished using morphological operations like opening and closing. Morphological operations are computationally expensive and moreover, they are difficult to run in parallel with connected component labeling routine since they need quite different type of processing from what connected component labeling does. In this paper, we first develop a fast and precise foreground mask correction method utilizing on neighbor pixel checking which is also employed in connected component labeling so that it can be incorporated into and run together with connected component labeling routine. Through experiments, it is verified that our proposed blob detection algorithm based on the foreground mask correction method developed in this paper shows better processing speed and more precise blob detection.