UnsupTrack: Simple Unsupervised Multi-Object Tracking

MOT17-01-DPM


Benchmark:

MOT17 | MOT16 | MOT20 |

Short name:

UnsupTrack

Detector:

Public

Description:

Multi-object tracking has seen a lot of progress recently, albeit with substantial annotation costs for developing better and larger labeled datasets. In this work, we remove the need for annotated datasets by proposing an unsupervised re-identification network, thus sidestepping the labeling costs entirely, required for training. Given unlabeled videos, our proposed method (SimpleReID) first generates tracking labels using SORT and trains a ReID network to predict the generated labels using crossentropy loss. We demonstrate that SimpleReID performs substantially better than simpler alternatives, and we recover the full performance of its supervised counterpart consistently across diverse tracking frameworks. The observations are unusual because unsupervised ReID is not expected to excel in crowded scenarios with occlusions, and drastic viewpoint changes. By incorporating our unsupervised SimpleReID with CenterTrack trained on augmented still images, we establish a new state-of-the-art performance on popular datasets like MOT16/17 without using tracking supervision, beating current best (CenterTrack) by 0.2-0.3 MOTA and 4.4-4.8 IDF1 scores. We further provide evidence for limited scope for improvement in IDF1 scores beyond our unsupervised ReID in the studied settings. Our investigation suggests reconsideration towards more sophisticated, supervised, end-to-end trackers by showing promise in simpler unsupervised alternatives.

Reference:

S. Karthik, A. Prabhu, V. Gandhi. Simple Unsupervised Multi-Object Tracking. In Arxiv, 2020.

Last submitted:

May 16, 2020 (1 month ago)

Published:

May 18, 2020 at 16:09:02 CET

Submissions:

1

Project page / code:

n/a

Open source:

No

Hardware:

GTX 1080Ti

Runtime:

2.0 Hz

Benchmark performance:

Sequence MOTA IDF1 MOTP MT ML FP FN Recall Precision FAF ID Sw. Frag
MOT1761.758.178.3640 (27.2)762 (32.4)16,872197,63265.095.61.01,864 (28.7)4,213 (64.8)

Detailed performance:

Sequence MOTA IDF1 MOTP MT ML FP FN Recall Precision FAF ID Sw. Frag
MOT17-01-DPM41.147.976.8795293,24549.785.81.22443
MOT17-01-FRCNN40.849.276.7896253,16650.984.01.42747
MOT17-01-SDP41.547.876.3986683,07552.383.51.53155
MOT17-03-DPM79.369.078.692111,19320,32080.698.60.8178471
MOT17-03-FRCNN79.969.678.69091,27819,55581.398.50.9179465
MOT17-03-SDP80.469.878.592101,35118,95981.998.40.9181473
MOT17-06-DPM53.040.877.458726324,81159.291.70.597203
MOT17-06-FRCNN54.543.777.064647464,51161.790.70.6110237
MOT17-06-SDP54.342.277.063647324,53761.590.80.6114228
MOT17-07-DPM44.945.075.611141,4837,72254.386.13.0103231
MOT17-07-FRCNN45.745.075.414141,4547,60955.086.52.9104236
MOT17-07-SDP45.345.275.414131,5677,55755.385.63.1111247
MOT17-08-DPM26.930.682.7103647814,86629.692.90.890150
MOT17-08-FRCNN26.530.182.9103645614,98029.193.10.783136
MOT17-08-SDP26.829.782.7103648814,88229.592.70.897148
MOT17-12-DPM46.544.781.817432784,33250.094.00.32448
MOT17-12-FRCNN46.646.481.817442574,34949.894.40.32346
MOT17-12-SDP46.345.781.817433104,32350.193.30.32449
MOT17-14-DPM32.240.373.9127865911,80436.191.00.973216
MOT17-14-FRCNN32.840.273.5137581811,50237.889.51.198244
MOT17-14-SDP32.441.173.5127487011,52737.688.91.293240

Raw data:


UnsupTrack