IEEE Trans Pattern Anal Mach Intell - Multiple Object Tracking Using K-Shortest Paths Optimization.

Tópicos

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Resumo

Multi-object tracking can be achieved by detecting objects in individual frames and then linking detections across frames. Such an approach can be made very robust to the occasional detection failure: If an object is not detected in a frame but is in previous and following ones, a correct trajectory will nevertheless be produced. By contrast, a false-positive detection in a few frames will be ignored. However, when dealing with a multiple target problem, the linking step results in a difficult optimization problem in the space of all possible families of trajectories. This is usually dealt with by sampling or greedy search based on variants of Dynamic Programming which can easily miss the global optimum. In this paper, we show that reformulating that step as a constrained flow optimization results in a convex problem. We take advantage of its particular structure to solve it using the k-shortest paths algorithm, which is very fast. This new approach is far simpler formally and algorithmically than existing techniques and lets us demonstrate excellent performance in two very different contexts.

Resumo Limpo

multiobject track can achiev detect object individu frame link detect across frame approach can made robust occasion detect failur object detect frame previous follow one correct trajectori will nevertheless produc contrast falseposit detect frame will ignor howev deal multipl target problem link step result difficult optim problem space possibl famili trajectori usual dealt sampl greedi search base variant dynam program can easili miss global optimum paper show reformul step constrain flow optim result convex problem take advantag particular structur solv use kshortest path algorithm fast new approach far simpler formal algorithm exist techniqu let us demonstr excel perform two differ context

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