136 research outputs found
DroTrack: High-speed Drone-based Object Tracking Under Uncertainty
We present DroTrack, a high-speed visual single-object tracking framework for
drone-captured video sequences. Most of the existing object tracking methods
are designed to tackle well-known challenges, such as occlusion and cluttered
backgrounds. The complex motion of drones, i.e., multiple degrees of freedom in
three-dimensional space, causes high uncertainty. The uncertainty problem leads
to inaccurate location predictions and fuzziness in scale estimations. DroTrack
solves such issues by discovering the dependency between object representation
and motion geometry. We implement an effective object segmentation based on
Fuzzy C Means (FCM). We incorporate the spatial information into the membership
function to cluster the most discriminative segments. We then enhance the
object segmentation by using a pre-trained Convolution Neural Network (CNN)
model. DroTrack also leverages the geometrical angular motion to estimate a
reliable object scale. We discuss the experimental results and performance
evaluation using two datasets of 51,462 drone-captured frames. The combination
of the FCM segmentation and the angular scaling increased DroTrack precision by
up to and decreased the centre location error by pixels on average.
DroTrack outperforms all the high-speed trackers and achieves comparable
results in comparison to deep learning trackers. DroTrack offers high frame
rates up to 1000 frame per second (fps) with the best location precision, more
than a set of state-of-the-art real-time trackers.Comment: 10 pages, 12 figures, FUZZ-IEEE 202
Support for energy-oriented design in the Australian context
There is a need for decision support tools that integrate energy simulation into early design in the context of Australian practice. Despite the proliferation of simulation programs in the last decade, there are no ready-to-use applications that cater specifically for the Australian climate and regulations. Furthermore, the majority of existing tools focus on achieving interaction with the design domain through model-based interoperability, and largely overlook the issue of process integration. This paper proposes an energy-oriented design environment that both accommodates the Australian context and provides interactive and iterative information exchanges that facilitate feedback between domains. It then presents the structure for DEEPA, an openly customisable system that couples parametric modelling and energy simulation software as a means of developing a decision support tool to allow designers to rapidly and flexibly assess the performance of early design alternatives. Finally, it discusses the benefits of developing a dynamic and concurrent performance evaluation process that parallels the characteristics and relationships of the design process
Closing the loop of design and analysis: Parametric modelling tools for early decision support
There is a growing need for parametric design software that communicates building performance feedback in early architectural exploration to support decision-making. This paper examines how the circuit of design and analysis process can be closed to provide active and concurrent feedback between architecture and services engineering domains. It presents the structure for an openly customisable design system that couples parametric modelling and energy analysis software to allow designers to assess the performance of early design iterations quickly. Finally, it discusses how user interactions with the system foster information exchanges that facilitate the sharing of design intelligence across disciplines
Utilizing Language Models for Energy Load Forecasting
Energy load forecasting plays a crucial role in optimizing resource
allocation and managing energy consumption in buildings and cities. In this
paper, we propose a novel approach that leverages language models for energy
load forecasting. We employ prompting techniques to convert energy consumption
data into descriptive sentences, enabling fine-tuning of language models. By
adopting an autoregressive generating approach, our proposed method enables
predictions of various horizons of future energy load consumption. Through
extensive experiments on real-world datasets, we demonstrate the effectiveness
and accuracy of our proposed method. Our results indicate that utilizing
language models for energy load forecasting holds promise for enhancing energy
efficiency and facilitating intelligent decision-making in energy systems.Comment: BuildSys 2023 Accepte
Human Mobility Question Answering (Vision Paper)
Question answering (QA) systems have attracted much attention from the
artificial intelligence community as they can learn to answer questions based
on the given knowledge source (e.g., images in visual question answering).
However, the research into question answering systems with human mobility data
remains unexplored. Mining human mobility data is crucial for various
applications such as smart city planning, pandemic management, and personalised
recommendation system. In this paper, we aim to tackle this gap and introduce a
novel task, that is, human mobility question answering (MobQA). The aim of the
task is to let the intelligent system learn from mobility data and answer
related questions. This task presents a new paradigm change in mobility
prediction research and further facilitates the research of human mobility
recommendation systems. To better support this novel research topic, this
vision paper also proposes an initial design of the dataset and a potential
deep learning model framework for the introduced MobQA task. We hope that this
paper will provide novel insights and open new directions in human mobility
research and question answering research
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