27 citations · 103 across the 12 of their papers we have counts for
9 papers · 1 filter
A time-weighted metric for sets of trajectories to assess multi-object tracking algorithms
Ángel F. García-Fernández, Abu Sajana Rahmathullah, Lennart Svensson
This paper proposes a metric for sets of trajectories to evaluate multi-object tracking algorithms that includes time-weighted costs for localisation errors of properly detected ta…
Extended Object Tracking Using Sets Of Trajectories with a PHD Filter
Jakob Sjudin, Martin Marcusson, Lennart Svensson +1
PHD filtering is a common and effective multiple object tracking (MOT) algorithm used in scenarios where the number of objects and their states are unknown. In scenarios where each…
DACS: Domain Adaptation via Cross-domain Mixed Sampling
Wilhelm Tranheden, Viktor Olsson, Juliano Pinto +1
Semantic segmentation models based on convolutional neural networks have recently displayed remarkable performance for a multitude of applications. However, these models typically…
ClassMix: Segmentation-Based Data Augmentation for Semi-Supervised Learning
Viktor Olsson, Wilhelm Tranheden, Juliano Pinto +1
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, p…
Trajectory Poisson multi-Bernoulli filters
Ángel F. García-Fernández, Lennart Svensson, Jason L. Williams +2
This paper presents two trajectory Poisson multi-Bernoulli (TPMB) filters for multi-target tracking: one to estimate the set of alive trajectories at each time step and another to…
Lidar-Camera Co-Training for Semi-Supervised Road Detection
Luca Caltagirone, Lennart Svensson, Mattias Wahde +1
Recent advances in the field of machine learning and computer vision have enabled the development of fast and accurate road detectors. Commonly such systems are trained within a su…