The KITTI Vision Benchmark Suite

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.

TBD

@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}

}