Outlier detection has recently become an important problem in many industrial and financial applications. Often, outliers have to be detected from data streams that continuously arrive from data sources. Incremental outlier detection algorithms, aimed at detecting outliers as soon as they appear in a database, have recently become emerging research field. In this paper, we develop an incremental version of connectivity-based outlier factor (COF) algorithm and discuss its computational complexity. The proposed incremental COF algorithm has equivalent detection performance as the iterated static COF algorithm (applied after insertion of each data record), with significant reduction in computational time. The paper provides theoretical and experimental evidence that the number of updates per such insertion/deletion does not depend on the total number of points in the data set, which makes algorithm viable for very large dynamic datasets. Finally, we also illustrate an application of the proposed algorithm on motion detection in video surveillance applications.