DensePeds uses Front-RVO motion prediction and Mask R-CNN sparse features to track people in crowds denser than 2 per square meter, running 4.5 times faster than prior methods and improving accuracy by 2.6% on dense datasets.
A Hybrid Data Association Framework for Robust Online Multi-Object Tracking
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abstract
Global optimization algorithms have shown impressive performance in data-association based multi-object tracking, but handling online data remains a difficult hurdle to overcome. In this paper, we present a hybrid data association framework with a min-cost multi-commodity network flow for robust online multi-object tracking. We build local target-specific models interleaved with global optimization of the optimal data association over multiple video frames. More specifically, in the min-cost multi-commodity network flow, the target-specific similarities are online learned to enforce the local consistency for reducing the complexity of the global data association. Meanwhile, the global data association taking multiple video frames into account alleviates irrecoverable errors caused by the local data association between adjacent frames. To ensure the efficiency of online tracking, we give an efficient near-optimal solution to the proposed min-cost multi-commodity flow problem, and provide the empirical proof of its sub-optimality. The comprehensive experiments on real data demonstrate the superior tracking performance of our approach in various challenging situations.
fields
cs.RO 1years
2019 1verdicts
UNVERDICTED 1representative citing papers
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DensePeds: Pedestrian Tracking in Dense Crowds Using Front-RVO and Sparse Features
DensePeds uses Front-RVO motion prediction and Mask R-CNN sparse features to track people in crowds denser than 2 per square meter, running 4.5 times faster than prior methods and improving accuracy by 2.6% on dense datasets.