Method

Rethink Multiple Object Tracking [Rethink MOT]


Submitted on 29 May. 2022 14:40 by
leichen wang (Robert Bosch CN)

Running time:0.3 s
Environment:4 cores @ 2.5 Ghz (Python)

Method Description:
In this work we rethink the widely used“tracking
by detection"(TBD) paradigm. We analyze several
typical tracking failure cases of existing TBD
algorithms.

To solve those failure cases, we design three
novel modules: 1) confidence-guided tracking
before detection module, 2) trajectory outlier
detection module, 3) motion-model-based trajectory
smoother.

Comprehensive experimental results on KITTI
Dataset and nuScenes Dataset shows that our final
method could achieve new SOTA results with these
plug-in modules.
Parameters:
alpha=0.2
Latex Bibtex:

Detailed Results

From all 29 test sequences, our benchmark computes the HOTA tracking metrics (HOTA, DetA, AssA, DetRe, DetPr, AssRe, AssPr, LocA) [1] as well as the CLEARMOT, MT/PT/ML, identity switches, and fragmentation [2,3] metrics. The tables below show all of these metrics.


Benchmark HOTA DetA AssA DetRe DetPr AssRe AssPr LocA
CAR 80.39 % 77.88 % 83.64 % 84.23 % 83.57 % 87.63 % 88.90 % 87.07 %

Benchmark TP FP FN
CAR 33094 1298 1569

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 91.53 % 85.58 % 91.66 % 46 77.65 %

Benchmark MT rate PT rate ML rate FRAG
CAR 89.39 % 6.31 % 4.31 % 134

Benchmark # Dets # Tracks
CAR 34663 688

This table as LaTeX