75,352 research outputs found
Information theoretic approach to robust multi-Bernoulli sensor control
A novel sensor control solution is presented, formulated within a
Multi-Bernoulli-based multi-target tracking framework. The proposed method is
especially designed for the general multi-target tracking case, where no prior
knowledge of the clutter distribution or the probability of detection profile
are available. In an information theoretic approach, our method makes use of
R\`{e}nyi divergence as the reward function to be maximized for finding the
optimal sensor control command at each step. We devise a Monte Carlo sampling
method for computation of the reward. Simulation results demonstrate successful
performance of the proposed method in a challenging scenario involving five
targets maneuvering in a relatively uncertain space with unknown
distance-dependent clutter rate and probability of detection
Cell Segmentation and Tracking using CNN-Based Distance Predictions and a Graph-Based Matching Strategy
The accurate segmentation and tracking of cells in microscopy image sequences
is an important task in biomedical research, e.g., for studying the development
of tissues, organs or entire organisms. However, the segmentation of touching
cells in images with a low signal-to-noise-ratio is still a challenging
problem. In this paper, we present a method for the segmentation of touching
cells in microscopy images. By using a novel representation of cell borders,
inspired by distance maps, our method is capable to utilize not only touching
cells but also close cells in the training process. Furthermore, this
representation is notably robust to annotation errors and shows promising
results for the segmentation of microscopy images containing in the training
data underrepresented or not included cell types. For the prediction of the
proposed neighbor distances, an adapted U-Net convolutional neural network
(CNN) with two decoder paths is used. In addition, we adapt a graph-based cell
tracking algorithm to evaluate our proposed method on the task of cell
tracking. The adapted tracking algorithm includes a movement estimation in the
cost function to re-link tracks with missing segmentation masks over a short
sequence of frames. Our combined tracking by detection method has proven its
potential in the IEEE ISBI 2020 Cell Tracking Challenge
(http://celltrackingchallenge.net/) where we achieved as team KIT-Sch-GE
multiple top three rankings including two top performances using a single
segmentation model for the diverse data sets.Comment: 25 pages, 14 figures, methods of the team KIT-Sch-GE for the IEEE
ISBI 2020 Cell Tracking Challeng
Tracking by Prediction: A Deep Generative Model for Mutli-Person localisation and Tracking
Current multi-person localisation and tracking systems have an over reliance
on the use of appearance models for target re-identification and almost no
approaches employ a complete deep learning solution for both objectives. We
present a novel, complete deep learning framework for multi-person localisation
and tracking. In this context we first introduce a light weight sequential
Generative Adversarial Network architecture for person localisation, which
overcomes issues related to occlusions and noisy detections, typically found in
a multi person environment. In the proposed tracking framework we build upon
recent advances in pedestrian trajectory prediction approaches and propose a
novel data association scheme based on predicted trajectories. This removes the
need for computationally expensive person re-identification systems based on
appearance features and generates human like trajectories with minimal
fragmentation. The proposed method is evaluated on multiple public benchmarks
including both static and dynamic cameras and is capable of generating
outstanding performance, especially among other recently proposed deep neural
network based approaches.Comment: To appear in IEEE Winter Conference on Applications of Computer
Vision (WACV), 201
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