IEEE Trans Pattern Anal Mach Intell - Continuous Energy Minimization for Multi-Target Tracking.

Tópicos

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Resumo

Many recent advances in multiple target tracking aim at finding a (nearly) optimal set of trajectories within a temporal window. To handle the large space of possible trajectory hypotheses, it is typically reduced to a finite set by some form of data-driven or regular discretization. In this work we propose an alternative formulation of multi-target tracking as minimization of a continuous energy. Contrary to recent approaches, we focus on designing an energy that corresponds to a more complete representation of the problem, rather than one that is amenable to global optimization. Besides the image evidence, the energy function takes into account physical constraints, such as target dynamics, mutual exclusion, and track persistence. In addition, partial image evidence is handled with explicit occlusion reasoning, and different targets are disambiguated with an appearance model. To nevertheless find strong local minima of the proposed non-convex energy we construct a suitable optimization scheme that alternates between continuous conjugate gradient descent and discrete trans-dimensional jump moves. These moves, which are executed such that they always reduce the energy, allow the search to escape weak minima and explore a much larger portion of the search space of varying dimensionality. We demonstrate the validity of our approach with an extensive quantitative evaluation on several public datasets.

Resumo Limpo

mani recent advanc multipl target track aim find near optim set trajectori within tempor window handl larg space possibl trajectori hypothes typic reduc finit set form datadriven regular discret work propos altern formul multitarget track minim continu energi contrari recent approach focus design energi correspond complet represent problem rather one amen global optim besid imag evid energi function take account physic constraint target dynam mutual exclus track persist addit partial imag evid handl explicit occlus reason differ target disambigu appear model nevertheless find strong local minima propos nonconvex energi construct suitabl optim scheme altern continu conjug gradient descent discret transdimension jump move move execut alway reduc energi allow search escap weak minima explor much larger portion search space vari dimension demonstr valid approach extens quantit evalu sever public dataset

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