OTWC: an efficient object-tracking method
Detection and tracking of moving objects is an important topic in computer vision and has turned into an active field of research with remarkable recent progress. This paper proposes OTWC; an efficient object-tracking method with Code Book (CB) model; a high-speed method with proper accuracy for detection and tracking moving objects. Our proposed method combines CB (Sigari and Fathy in Proceedings of the international multiconference of engineers and computer scientists, pp 1921, 2008) algorithm, density-based spatial clustering of applications with noise (DBSCAN) (Ester et al. in Proceedings of 24th VLDB conference, 1998) clustering algorithm, and incremental bulk DBSCAN algorithm in order to effectively track moving objects. We have claimed that CB algorithm models the background and foreground simultaneously so that it is possible to use the wasted information in the background estimation step in order to incrementally detect and track the foreground. We have also compared our method with other available alternatives including a general algorithm for tracking two datasets. The results demonstrate that our proposed algorithm has an acceptable performance in terms of both accuracy and speed.
incremental bulk dbscan algorithm object detection and tracking code book model video analysis