 We propose a self-supervised method for incrementally refining visual descriptors to improve performance in the task of object-level visual data association. Our method optimizes deep descriptor generators online, by fine-tuning a widely available  network pre-trained for image classification . We show that earlier layers in the network outperform later-stage layers for the data association task while also allowing for a 94\% reduction in the number of parameters, enabling the online optimization. We show that choosing positive examples separated by large temporal distances and negative examples close in the descriptor space improves the quality of the learned descriptors for the multi-object tracking task. Finally, we demonstrate a MOTA score of 21.25\% on the 2D-MOT-2015 dataset using visual information alone, outperforming methods that incorporate motion information.
Object-level data association is central to robotic applications such as tracking-by-detection and object-level simultaneous localization and mapping. While current learned visual data association methods outperform hand-crafted algorithms, many rely on large collections of domain-specific training examples that can be difficult to obtain without prior knowledge. Additionally, such methods often remain fixed during inference-time and do not harness observed information to better their performance. We propose a self-supervised method for incrementally refining visual descriptors to improve performance in the task of object-level visual data association. Our method optimizes deep descriptor generators online, by continuously training a widely available image classification network pre-trained with domain-independent data . We show that earlier layers in the network outperform later-stage layers for the data association task while also allowing for a 94\% reduction in the number of parameters, enabling the online optimization. We show that self-labelling challenging triplets--choosing positive examples separated by large temporal distances and negative examples close in the descriptor space--improves the quality of the learned descriptors for the multi-object tracking task. Finally, we demonstrate that our approach surpasses other visual data-association methods applied to a tracking-by-detection task, and show that it provides better performance-gains when compared to other methods that attempt to adapt to observed information.
A
Object-level data association is central to robotic applications such as tracking-by-detection and object-level simultaneous localization and mapping. While current learned visual data association methods outperform hand-crafted algorithms, many rely on large collections of domain-specific training examples that can be difficult to obtain without prior knowledge. Additionally, such methods often remain fixed during inference-time and do not harness observed information to better their performance.
R
fine-tuning
continuously training
A
image classification
R
for image classification
with domain-independent data
R
choosing
self-labelling challenging triplets--choosing
R
space improves
space--improves
R
a MOTA score of 21.25\% on the 2D-MOT-2015 dataset using visual information alone, outperforming methods that incorporate motion
that our approach surpasses other visual data-association methods applied to a tracking-by-detection task, and show that it provides better performance-gains when compared to other methods that attempt to adapt to observed