30,226 research outputs found
Dynamic Objects Segmentation for Visual Localization in Urban Environments
Visual localization and mapping is a crucial capability to address many
challenges in mobile robotics. It constitutes a robust, accurate and
cost-effective approach for local and global pose estimation within prior maps.
Yet, in highly dynamic environments, like crowded city streets, problems arise
as major parts of the image can be covered by dynamic objects. Consequently,
visual odometry pipelines often diverge and the localization systems
malfunction as detected features are not consistent with the precomputed 3D
model. In this work, we present an approach to automatically detect dynamic
object instances to improve the robustness of vision-based localization and
mapping in crowded environments. By training a convolutional neural network
model with a combination of synthetic and real-world data, dynamic object
instance masks are learned in a semi-supervised way. The real-world data can be
collected with a standard camera and requires minimal further post-processing.
Our experiments show that a wide range of dynamic objects can be reliably
detected using the presented method. Promising performance is demonstrated on
our own and also publicly available datasets, which also shows the
generalization capabilities of this approach.Comment: 4 pages, submitted to the IROS 2018 Workshop "From Freezing to
Jostling Robots: Current Challenges and New Paradigms for Safe Robot
Navigation in Dense Crowds
Image informatics strategies for deciphering neuronal network connectivity
Brain function relies on an intricate network of highly dynamic neuronal connections that rewires dramatically under the impulse of various external cues and pathological conditions. Among the neuronal structures that show morphologi- cal plasticity are neurites, synapses, dendritic spines and even nuclei. This structural remodelling is directly connected with functional changes such as intercellular com- munication and the associated calcium-bursting behaviour. In vitro cultured neu- ronal networks are valuable models for studying these morpho-functional changes. Owing to the automation and standardisation of both image acquisition and image analysis, it has become possible to extract statistically relevant readout from such networks. Here, we focus on the current state-of-the-art in image informatics that enables quantitative microscopic interrogation of neuronal networks. We describe the major correlates of neuronal connectivity and present workflows for analysing them. Finally, we provide an outlook on the challenges that remain to be addressed, and discuss how imaging algorithms can be extended beyond in vitro imaging studies
Extraction and Classification of Diving Clips from Continuous Video Footage
Due to recent advances in technology, the recording and analysis of video
data has become an increasingly common component of athlete training
programmes. Today it is incredibly easy and affordable to set up a fixed camera
and record athletes in a wide range of sports, such as diving, gymnastics,
golf, tennis, etc. However, the manual analysis of the obtained footage is a
time-consuming task which involves isolating actions of interest and
categorizing them using domain-specific knowledge. In order to automate this
kind of task, three challenging sub-problems are often encountered: 1)
temporally cropping events/actions of interest from continuous video; 2)
tracking the object of interest; and 3) classifying the events/actions of
interest.
Most previous work has focused on solving just one of the above sub-problems
in isolation. In contrast, this paper provides a complete solution to the
overall action monitoring task in the context of a challenging real-world
exemplar. Specifically, we address the problem of diving classification. This
is a challenging problem since the person (diver) of interest typically
occupies fewer than 1% of the pixels in each frame. The model is required to
learn the temporal boundaries of a dive, even though other divers and
bystanders may be in view. Finally, the model must be sensitive to subtle
changes in body pose over a large number of frames to determine the
classification code. We provide effective solutions to each of the sub-problems
which combine to provide a highly functional solution to the task as a whole.
The techniques proposed can be easily generalized to video footage recorded
from other sports.Comment: To appear at CVsports 201
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