21,599 research outputs found
Learning to Divide and Conquer for Online Multi-Target Tracking
Online Multiple Target Tracking (MTT) is often addressed within the
tracking-by-detection paradigm. Detections are previously extracted
independently in each frame and then objects trajectories are built by
maximizing specifically designed coherence functions. Nevertheless, ambiguities
arise in presence of occlusions or detection errors. In this paper we claim
that the ambiguities in tracking could be solved by a selective use of the
features, by working with more reliable features if possible and exploiting a
deeper representation of the target only if necessary. To this end, we propose
an online divide and conquer tracker for static camera scenes, which partitions
the assignment problem in local subproblems and solves them by selectively
choosing and combining the best features. The complete framework is cast as a
structural learning task that unifies these phases and learns tracker
parameters from examples. Experiments on two different datasets highlights a
significant improvement of tracking performances (MOTA +10%) over the state of
the art
The Greedy Dirichlet Process Filter - An Online Clustering Multi-Target Tracker
Reliable collision avoidance is one of the main requirements for autonomous
driving. Hence, it is important to correctly estimate the states of an unknown
number of static and dynamic objects in real-time. Here, data association is a
major challenge for every multi-target tracker. We propose a novel multi-target
tracker called Greedy Dirichlet Process Filter (GDPF) based on the
non-parametric Bayesian model called Dirichlet Processes and the fast posterior
computation algorithm Sequential Updating and Greedy Search (SUGS). By adding a
temporal dependence we get a real-time capable tracking framework without the
need of a previous clustering or data association step. Real-world tests show
that GDPF outperforms other multi-target tracker in terms of accuracy and
stability
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