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''Target representation and localization'' is mostly a bottom-up process. These methods give a variety of tools for identifying the moving object. Locating and tracking the target object successfully is dependent on the algorithm. For example, using blob tracking is useful for identifying human movement because a person's profile changes dynamically.<ref>{{cite journal |author1=S. Kang |author2=J. Paik |author3=A. Koschan |author4=B. Abidi |author5=M. A. Abidi |last-author-amp=yes |title=Real-time video tracking using PTZ cameras |journal=Proc. SPIE |volume= 5132 |pages=103–111 |year=2003 |citeseerx=10.1.1.101.4242 |doi=10.1117/12.514945 }}</ref> Typically the computational complexity for these algorithms is low. The following are some common ''target representation and localization'' algorithms:
* '''Kernel-based tracking''' ([[mean-shift]] tracking<ref>Comaniciu, D.; Ramesh, V.; Meer, P., "Real-time tracking of non-rigid objects using mean shift," Computer Vision and Pattern Recognition, 2000. Proceedings. IEEE Conference on , vol.2, no., pp.142,149 vol.2, 2000</ref>): an iterative localization procedure based on the maximization of a [[similarity measure]] ([[Bhattacharyya coefficient]]).
* '''Contour tracking''': detection of object boundary (e.g. active contours or [[Condensation algorithm]]). Contour tracking methods iteratively evolve an initial contour initialized from the previous frame to its new position in the current frame. This approach to contour tracking directly evolves the contour by minimizing the contour energy using gradient descent.
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