81 research outputs found
SeaDSC: A video-based unsupervised method for dynamic scene change detection in unmanned surface vehicles
Recently, there has been an upsurge in the research on maritime vision, where
a lot of works are influenced by the application of computer vision for
Unmanned Surface Vehicles (USVs). Various sensor modalities such as camera,
radar, and lidar have been used to perform tasks such as object detection,
segmentation, object tracking, and motion planning. A large subset of this
research is focused on the video analysis, since most of the current vessel
fleets contain the camera's onboard for various surveillance tasks. Due to the
vast abundance of the video data, video scene change detection is an initial
and crucial stage for scene understanding of USVs. This paper outlines our
approach to detect dynamic scene changes in USVs. To the best of our
understanding, this work represents the first investigation of scene change
detection in the maritime vision application. Our objective is to identify
significant changes in the dynamic scenes of maritime video data, particularly
those scenes that exhibit a high degree of resemblance. In our system for
dynamic scene change detection, we propose completely unsupervised learning
method. In contrast to earlier studies, we utilize a modified cutting-edge
generative picture model called VQ-VAE-2 to train on multiple marine datasets,
aiming to enhance the feature extraction. Next, we introduce our innovative
similarity scoring technique for directly calculating the level of similarity
in a sequence of consecutive frames by utilizing grid calculation on retrieved
features. The experiments were conducted using a nautical video dataset called
RoboWhaler to showcase the efficient performance of our technique.Comment: WACV 2024 conferenc
Learning to Communicate Using Counterfactual Reasoning
This paper introduces a new approach for multi-agent communication learning
called multi-agent counterfactual communication (MACC) learning. Many
real-world problems are currently tackled using multi-agent techniques.
However, in many of these tasks the agents do not observe the full state of the
environment but only a limited observation. This absence of knowledge about the
full state makes completing the objectives significantly more complex or even
impossible. The key to this problem lies in sharing observation information
between agents or learning how to communicate the essential data. In this paper
we present a novel multi-agent communication learning approach called MACC. It
addresses the partial observability problem of the agents. MACC lets the agent
learn the action policy and the communication policy simultaneously. We focus
on decentralized Markov Decision Processes (Dec-MDP), where the agents have
joint observability. This means that the full state of the environment can be
determined using the observations of all agents. MACC uses counterfactual
reasoning to train both the action and the communication policy. This allows
the agents to anticipate on how other agents will react to certain messages and
on how the environment will react to certain actions, allowing them to learn
more effective policies. MACC uses actor-critic with a centralized critic and
decentralized actors. The critic is used to calculate an advantage for both the
action and communication policy. We demonstrate our method by applying it on
the Simple Reference Particle environment of OpenAI and a MNIST game. Our
results are compared with a communication and non-communication baseline. These
experiments demonstrate that MACC is able to train agents for each of these
problems with effective communication policies.Comment: Submitted to NeurIPS2020. Contains 10 pages with 9 figures and 4
appendice
Vehicular communication management framework : a flexible hybrid connectivity platform for CCAM services
In the upcoming decade and beyond, the Cooperative, Connected and Automated Mobility (CCAM) initiative will play a huge role in increasing road safety, traffic efficiency and comfort of driving in Europe. While several individual vehicular wireless communication technologies exist, there is still a lack of real flexible and modular platforms that can support the need for hybrid communication. In this paper, we propose a novel vehicular communication management framework (CAMINO), which incorporates flexible support for both short-range direct and long-range cellular technologies and offers built-in Cooperative Intelligent Transport Systems' (C-ITS) services for experimental validation in real-life settings. Moreover, integration with vehicle and infrastructure sensors/actuators and external services is enabled using a Distributed Uniform Streaming (DUST) framework. The framework is implemented and evaluated in the Smart Highway test site for two targeted use cases, proofing the functional operation in realistic environments. The flexibility and the modular architecture of the hybrid CAMINO framework offers valuable research potential in the field of vehicular communications and CCAM services and can enable cross-technology vehicular connectivity
The Second Monocular Depth Estimation Challenge
This paper discusses the results for the second edition of the Monocular
Depth Estimation Challenge (MDEC). This edition was open to methods using any
form of supervision, including fully-supervised, self-supervised, multi-task or
proxy depth. The challenge was based around the SYNS-Patches dataset, which
features a wide diversity of environments with high-quality dense ground-truth.
This includes complex natural environments, e.g. forests or fields, which are
greatly underrepresented in current benchmarks.
The challenge received eight unique submissions that outperformed the
provided SotA baseline on any of the pointcloud- or image-based metrics. The
top supervised submission improved relative F-Score by 27.62%, while the top
self-supervised improved it by 16.61%. Supervised submissions generally
leveraged large collections of datasets to improve data diversity.
Self-supervised submissions instead updated the network architecture and
pretrained backbones. These results represent a significant progress in the
field, while highlighting avenues for future research, such as reducing
interpolation artifacts at depth boundaries, improving self-supervised indoor
performance and overall natural image accuracy.Comment: Published at CVPRW202
A New Hybrid Approach on WCET Analysis for Real-Time Systems Using Machine Learning
The notion of the Worst-Case Execution Time (WCET) allows system engineers to create safe real-time systems. This value is used to schedule all software tasks before their deadlines. Failing these deadlines will cause catastrophic events, e.g. vehicle crashes, failing to detect dangerous anomalies, etc. Different analysis methodologies exist to determine the WCET. However, these methods do not provide early insight in the WCET during development. Therefore, pessimistic assumptions are made by system designers resulting in more expensive, overqualified hardware.
In this paper, an extension on the hybrid methodology is proposed which implements a predictor model using Machine Learning (ML). This new approach estimates the WCET on smaller entities of the code, so-called hybrid blocks, based on software and hardware features. As a result, the ML-based hybrid analysis provides insight of the WCET early-on in the development process and refines its estimate when more detailed features are available. In order to facilitate the extraction of code-related features, a new tool for the COBRA framework is proposed.
This paper proves the potential of the ML-based hybrid approach by conducting multiple experiments based on the TACLeBench on a first prototype. A set of annotated code features were used to train and validate eight different regression models. The results already show promising estimates without tuning any hyperparameters, proving the potential of the methodology
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