Benchmark:

MOT15 |

Short name:

SMOT

Detector:

Public

Description:

We introduce a computationally efficient algorithm for
multi-object tracking by detection that addresses four main
challenges: appearance similarity among targets, missing
data due to targets being out of the field of view or oc-
cluded behind other objects, crossing trajectories, and cam-
era motion. The proposed method uses motion dynamics as
a cue to distinguish targets with similar appearance, min-
imize target mis-identification and recover missing data.
Computational efficiency is achieved by using a General-
ized Linear Assignment (GLA) coupled with efficient proce-
dures to recover missing data and estimate the complexity
of the underlying dynamics. The proposed approach works
with tracklets of arbitrary length and does not assume a
dynamical model a priori, yet it captures the overall mo-
tion dynamics of the targets. Experiments using challenging
videos show that this framework can handle complex target
motions, non-stationary cameras and long occlusions, on
scenarios where appearance cues are not available or poor.

[Parameters]
min_s=0.017883
mota_th=0.52953
hor=13.3269
eta_max=0.72506

Reference:

C. Dicle, O. Camps, M. Sznaier. The Way They Move: Tracking Targets with Similar Appearance. In ICCV, 2013.

Last submitted:

February 20, 2015 (9 years ago)

Published:

November 01, 2014 at 03:09:33 CET

Submissions:

1

Project page / code:

https://bitbucket.org/cdicle/smot

Open source:

Yes

Hardware:

2.6 GHz, 16 Cores

Runtime:

2.7 Hz

Benchmark performance:

Detailed performance:

Raw data:

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