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A video sequence is in principle a sequence of images. The methods presented in the previous chapters therefore apply equally well to a video sequence as to an image. One image is simply processed at a time. There are, however, two differences between a video sequence and an image. First, working with video allows us to consider temporal information and hence segment objects based on their motion. Second, video acquisition and image acquisition may not be the same, and that can have some consequences. The latter is first considered by describing the notion of the framerate of the camera together with how video data is compressed. Next the chapter details the most fundamental segmentation algorithm related to video data, namely background subtraction. The principal of the core functionality is laid out followed by different schemes of optimizing the method. Finally the related image differencing method is presented.
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Casado, I.H., Holte, M.B., Moeslund, T.B., Gonzalez, J.: Detection and removal of chromatic moving shadows in surveillance scenarios. In: International Conference on Computer Vision, Kyoto, Japan, October 2009
Elgammal, A.: Figure-ground segmentation—pixel-based. In: Moeslund, T.B., Hilton, A., Kruger, V., Sigal, L. (eds.) Visual Analysis of Humans—Looking at People. Springer, Berlin (2011). 978-0-85729-996-3
Kim, K., Chalidabhongse, T.H., Harwood, D., Davis, L.: Real-time foreground-background segmentation using codebook model. Real-Time Imaging 11(3), 167–256 (2005) CrossRef
Prati, A., Mikic, I., Trivedi, M.M., Cucchiara, R.: Detecting moving shadows: algorithms and evaluation. IEEE Trans. Pattern Anal. Mach. Intell. 25(7), 918–923 (2003) CrossRef
Shi, Y.Q., Sun, H.: Image and Video Compression for Multimedia for Engineering: Fundamentals, Algorithms, and Standards. CRC Press, Boca Raton (2000).
Stauffer, C., Grimson, W.E.L.: Adaptive background mixture models for real-time tracking. In: IEEE Conference on Computer Vision and Pattern Recognition, Ft. Collins, CO, USA, June 1999
- Segmentation in Video Data
Thomas B. Moeslund
- Springer London
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