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Extra resources for A Theory of Differentiation in Locally Convex Spaces / Memoirs No. 212 (Memoirs of the American Mathematical Society)
2010] for modeling discriminative appearance affinities across cameras. 18 2. WIDE-AREA TRACKING As the hand-labeled correspondences are not available at runtime, collecting training examples is difficult. , 2010], the spatio-temporal constraints are considered to collect online training samples, which are termed as “weakly labeled samples". A training sample is defined as a pair of tracklets from two cameras. Negative samples are the pairs of tracklets which overlap in time. Pairs of tracklets which have the potential to link constitute a positive “bag".
14) λq Metropolis-Hastings based Adaptation of Tracklet Association Whenever there is a peak in the TAC function for some edge along a path, the validity of the connections between the features along that path is under doubt. As per the Metropolis-Hastings method, this leads to a new candidate affinity score sij on this edge where the peak occurs using a proposal distribution qaf (sij |sij ), where sij is the affinity score on edge eij . , U (sij − δ, sij + δ), since without additional information, uniform distribution can be a reasonable guess of the new weights.
Next, through communication between fusion centers, this graph matching framework can be applied to associate these super-tracklets and end up with the final tracks over the network. 2. , 2003]. However, transferring particles and their weights across the network may introduce heavy communication load. It can be more efficient, in term of communication expense, to run one particle filter at the fusion center instead of running a particle filter at each camera node. Cameras transmit their measurements to associated fusion centers.