Method

TripletTrack [TripletTrack]


Submitted on 16 Mar. 2022 21:40 by
Nicola Marinello (KU Leuven)

Running time:0.1 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
We start from an off-the-shelf 3D object detector, and apply a
tracking mechanism where objects are matched by an affinity score
computed on local object feature embeddings and motion
descriptors. The feature embeddings are trained to include
information about the visual appearance and monocular 3D object
characteristics, while motion descriptors provide a strong
representation of object trajectories.
Parameters:
TBD
Latex Bibtex:
@InProceedings{Marinello_2022_CVPR,

author = {Marinello, Nicola and Proesmans, Marc and Van
Gool, Luc},

title = {TripletTrack: 3D Object Tracking Using Triplet
Embeddings and LSTM},

booktitle = {Proceedings of the IEEE/CVF Conference on
Computer Vision and Pattern Recognition (CVPR) Workshops},

month = {June},

year = {2022},

pages = {4500-4510}

}

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 73.58 % 73.18 % 74.66 % 76.18 % 86.81 % 77.31 % 89.55 % 87.37 %
PEDESTRIAN 42.77 % 39.54 % 46.54 % 41.97 % 71.91 % 50.86 % 71.26 % 77.93 %

Benchmark TP FP FN
CAR 29750 4642 430
PEDESTRIAN 12714 10436 798

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 84.32 % 86.06 % 85.25 % 322 72.26 %
PEDESTRIAN 50.08 % 73.84 % 51.47 % 323 35.71 %

Benchmark MT rate PT rate ML rate FRAG
CAR 69.85 % 26.31 % 3.85 % 522
PEDESTRIAN 24.05 % 46.73 % 29.21 % 1049

Benchmark # Dets # Tracks
CAR 30180 854
PEDESTRIAN 13512 374

This table as LaTeX