HybridDAT: A Hybrid Data Association Framework for Robust Online Multi-Object Tracking


Benchmark:

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.

Project page / code:

n/a

Reference:

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

Processing:

Online

Last submitted:

May 30, 2016 (3 years ago)

Published:

August 24, 2017 at 14:20:15 CET

Submissions:

2

Open source:

No

Hardware:

3.4 GHZ, 1 Core

Benchmark performance:

Sequence MOTA IDF1 MOTP MT ML FP FN ID Sw.
2D MOT 201535.047.772.682.0304.08,45531,140358

Detailed performance:

Sequence MOTA IDF1 MOTP MT ML FP FN ID Sw.
ADL-Rundle-130.648.872.49.02.02,8273,58252
ADL-Rundle-334.143.878.46.08.01,5605,08949
AVG-TownCentre29.244.469.021.098.05324,46561
ETH-Crossing24.141.474.72.016.0297311
ETH-Jelmoli41.856.973.08.015.02731,19310
ETH-Linthescher27.137.776.710.0139.0876,40912
KITTI-1646.365.071.71.03.019870610
KITTI-1927.553.466.37.013.01,3102,53527
PETS09-S2L247.746.069.35.04.08174,112110
TUD-Crossing73.370.473.68.02.0422448
Venice-137.153.674.65.04.07802,07418

Raw data:


HybridDAT