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Multi-object tracking (MOT) is the task of estimating the state trajectories of an unknown and time-varying number of objects over a certain time window. Several algorithms have been proposed to tackle the multi-object smoothing task, where object de
Externí odkaz:
http://arxiv.org/abs/2312.17261
Autor:
Pinto, Juliano Ventura
A disponibilidade de hemoderivados é um parâmetro importante para medir a qualidade da saúde em um país. Dentre os produtos hemoderivados, imunoglobulinas tem alto valor agregado. O Instituto Butantan tem por objetivo o estabelecimento de uma pla
Autor:
Pinto, Juliano, Hess, Georg, Ljungbergh, William, Xia, Yuxuan, Wymeersch, Henk, Svensson, Lennart
Multi-object tracking (MOT) is the problem of tracking the state of an unknown and time-varying number of objects using noisy measurements, with important applications such as autonomous driving, tracking animal behavior, defense systems, and others.
Externí odkaz:
http://arxiv.org/abs/2202.07909
Autor:
Dixon, Clara M., Schorne-Pinto, Juliano, Aziziha, Mina, Yingling, Jacob A., Booth, Ronald E., Besmann, Theodore M.
Publikováno v:
In Journal of Molecular Liquids 15 July 2024 406
Evaluating the performance of multi-object tracking (MOT) methods is not straightforward, and existing performance measures fail to consider all the available uncertainty information in the MOT context. This can lead practitioners to select models wh
Externí odkaz:
http://arxiv.org/abs/2108.04619
Autor:
Pinto, Juliano, Hess, Georg, Ljungbergh, William, Xia, Yuxuan, Svensson, Lennart, Wymeersch, Henk
Multitarget Tracking (MTT) is the problem of tracking the states of an unknown number of objects using noisy measurements, with important applications to autonomous driving, surveillance, robotics, and others. In the model-based Bayesian setting, the
Externí odkaz:
http://arxiv.org/abs/2104.00734
Semantic segmentation models based on convolutional neural networks have recently displayed remarkable performance for a multitude of applications. However, these models typically do not generalize well when applied on new domains, especially when go
Externí odkaz:
http://arxiv.org/abs/2007.08702
The state of the art in semantic segmentation is steadily increasing in performance, resulting in more precise and reliable segmentations in many different applications. However, progress is limited by the cost of generating labels for training, whic
Externí odkaz:
http://arxiv.org/abs/2007.07936
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