The KITTI Vision Benchmark Suite

Tracking using optical flow, appearance vector,
intersection over union and pose estimation.
Hungarian method association

Score>0.5

@InProceedings{10.1007/978-3-030-50516-5_5,
author="Gonzalez, Nicolas Franco
and Ospina, Andres
and Calvez, Philippe",
editor="Campilho, Aur{\'e}lio
and Karray, Fakhri
and Wang, Zhou",
title="SMAT: Smart Multiple Affinity Metrics for
Multiple Object Tracking",
booktitle="Image Analysis and Recognition",
year="2020",
publisher="Springer International Publishing",
address="Cham",
pages="48--62",
abstract="This research introduces a novel
multiple object tracking algorithm called SMAT
(Smart Multiple Affinity Metric Tracking) that
works as an online tracking-by-detection
approach. The use of various characteristics from
observation is established as a critical factor
for improving tracking performance. By using the
position, motion, appearance, and a correction
component, our approach achieves an accuracy
comparable to state of the art trackers. We use
the optical flow to track the motion of the
objects, we show that tracking accuracy can be
improved by using a neural network to select key
points to be tracked by the optical flow. The
proposed algorithm is evaluated by using the
KITTI Tracking Benchmark for the class CAR.",
isbn="978-3-030-50516-5"
}