Model-free tracking is important for solving tasks such as moving-object tracking
and action recognition in cases where no prior object knowledge is available. For this
purpose, we extend the concept of spatially synchronous dynamics in spin-lattice models to the spatiotemporal domain to track segments within an image sequence. The method is related to synchronization processes in neural networks and based on superparamagnetic clustering
of data. Spin interactions result in the formation of clusters of correlated spins, providing an automatic labeling of corresponding image regions. The algorithm obeys detailed balance.
This is an important property as it allows for consistent spin-transfer across subsequent
frames, which can be used for segment tracking. Therefore, in the tracking process the correct
equilibrium will always be found, which is an important advance as compared with other more
heuristic tracking procedures. In the case of long image sequences, i.e., movies, the algorithm
is augmented with a feedback mechanism, further stabilizing segment tracking.
CitationDellen, B.; Aksoy , E.; Wörgötter , F. Segment tracking via a spatiotemporal linking process including feedback stabilization in an n-D lattice model. "Sensors", 2009, vol. 9, núm. 11, p. 9355-9379.
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