LP_SSVM: Linear Programming Learned With Structured SVM

KITTI-16


Benchmark:

MOT15 |

Short name:

LP_SSVM

Detector:

Public

Description:

Min-cost network flow model with pairwise interactions; network cost is learned using structured SVM.

Reference:

S. Wang, C. Fowlkes. Learning Optimal Parameters for Multi-target Tracking with Contextual Interactions. In International Journal of Computer Vision, 2016.

Last submitted:

April 20, 2015 (6 years ago)

Published:

April 21, 2015 at 04:22:07 CET

Submissions:

1

Project page / code:

n/a

Open source:

No

Hardware:

2.4GHz, 1 Core

Runtime:

41.3 Hz

Benchmark performance:

Sequence MOTA IDF1 HOTA MT ML FP FN Rcll Prcn AssA DetA AssRe AssPr DetRe DetPr LocA FAF ID Sw. Frag
MOT1525.234.026.342 (5.8)382 (53.0)8,36936,93239.974.526.326.729.066.230.456.975.61.4646 (16.2)849 (21.3)

Detailed performance:

Sequence MOTA IDF1 HOTA MT ML FP FN Rcll Prcn AssA DetA AssRe AssPr DetRe DetPr LocA FAF ID Sw. Frag
ADL-Rundle-114.038.330.2773,5074,42352.558.232.228.935.069.340.044.475.97.06992
ADL-Rundle-328.036.928.54101,8555,38847.072.027.130.529.069.036.255.476.23.08183
AVG-TownCentre14.724.319.761394595,51522.878.123.416.928.753.617.961.073.71.0123141
ETH-Crossing24.937.526.01171074126.196.334.319.935.380.620.375.079.20.022
ETH-Jelmoli39.552.435.58162241,29349.084.736.734.641.069.838.165.977.90.51729
ETH-Linthescher15.623.521.05157417,48316.297.235.712.437.976.012.575.379.40.01115
KITTI-1639.241.727.3029092445.789.623.332.326.957.934.768.077.40.42029
KITTI-1928.239.627.14188102,95544.774.725.329.629.758.733.355.671.90.870162
PETS09-S2L241.527.921.1376294,80350.288.512.935.213.959.837.666.274.61.4212249
TUD-Crossing60.055.038.3424837566.093.831.846.238.657.349.870.877.70.21820
Venice-117.831.925.0076963,03233.668.728.022.528.976.926.153.476.61.52327

Raw data: