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Multi-object tracking remains challenging due to frequent occurrence ofocclusions and outliers. In order to handle this problem, we propose anApproximation-Shrink Scheme for sequential optimization. This scheme isrealized by introducing an Ambiguity-Clearness Graph to avoid conflicts andmaintain sequence independent, as well as a sliding window optimizationframework to constrain the size of state space and guarantee convergence. Basedon this window-wise framework, the states of targets are clustered in aself-organizing manner. Moreover, we show that the traditional online and batchtracking methods can be embraced by the window-wise framework. Experimentsindicate that with only a small window, the optimization performance can bemuch better than online methods and approach to batch methods.

Sliding-Window Optimization on an Ambiguity-Clearness Graph for Multi-object Tracking