Benchmark:

MOT15 |

Short name:

HybridDAT

Detector:

Public

Description:

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 mincost 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.

Reference:

M. Yang, Y. Jia. A Hybrid Data Association Framework for Robust Online Multi-Object Tracking. In IEEE Transactions on Image Processing, 2016.

Last submitted:

May 30, 2016 (7 years ago)

Published:

August 24, 2017 at 14:20:15 CET

Submissions:

2

Project page / code:

n/a

Open source:

No

Hardware:

3.4 GHZ, 1 Core

Runtime:

4.6 Hz

Benchmark performance:

Detailed performance:

Raw data:

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