Tracking-by-X

TUD-Crossing PETS09-S2L2 ETH-Jelmoli ETH-Linthescher ETH-Crossing AVG-TownCentre ADL-Rundle-1 ADL-Rundle-3 KITTI-16 KITTI-19

Short name:

TBX

Benchmark:

Description:

We present a novel formulation of the multiple object tracking problem which integrates low and mid-level features. In particular, we formulate the tracking problem as a quadratic program coupling detections and dense point trajectories. Due to the computational complexity of the initial QP, we propose an approximation by two auxiliary problems, a temporal and spatial association, where the temporal subproblem can be efficiently solved by a linear program and the spatial association by a clustering algorithm. The objective function of the QP is used in order to find the optimal number of clusters, where each cluster ideally represents one person. Evaluation is provided for multiple scenarios, showing the superiority of our method with respect to classic tracking-by-detection methods and also other methods that greedily integrate low-level features.

Hardware:

3.50GHz, 1 Core

Detector:

Public

Processing:

Batch

Last submitted:

March 14, 2016 (1 year ago)

Published:

November 06, 2015 at 23:40:14 CET

Submissions:

2

Open source:

No

Project page / code:

Reference:

R. Henschel, L. Leal-Taixé, B. Rosenhahn, K. Schindler. Tracking with multi-level features. In arXiv:1607.07304, 2016.

Benchmark performance:

MOTAMOTPFAFMTMLFPFNID Sw.FragSpecificationsDetector
27.570.61.410.4 % 45.8 % 7,96835,8107591,5283.50GHz, 1 CorePublic
IDF1ID PrecisionID Recall
33.847.926.2

Detailed performance:

Sequence MOTA IDF1 MOTP FAF GT MT ML FP FN ID Sw Frag
TUD-Crossing58.356.972.80.71353.8 % 7.7 % 1502822825
PETS09-S2L241.833.268.93.2429.5 % 4.8 % 1,3913,949269496
ETH-Jelmoli46.758.872.60.34513.3 % 37.8 % 1151,2201671
ETH-Linthescher16.124.274.40.11972.0 % 81.2 % 1317,3451243
ETH-Crossing20.125.673.70.2263.8 % 73.1 % 3576067
AVG-TownCentre29.130.568.62.022616.4 % 35.8 % 9153,956199378
ADL-Rundle-116.937.171.75.23215.6 % 34.4 % 2,5935,08952123
ADL-Rundle-329.128.872.52.1449.1 % 25.0 % 1,2895,8378569
KITTI-1640.243.371.60.9170.0 % 11.8 % 1907953266
KITTI-1923.443.865.61.06211.3 % 25.8 % 1,0462,99851233
Venice-118.921.672.40.3170.0 % 58.8 % 1133,579917

Raw data:


TBX