1,437 research outputs found
Am I Done? Predicting Action Progress in Videos
In this paper we deal with the problem of predicting action progress in
videos. We argue that this is an extremely important task since it can be
valuable for a wide range of interaction applications. To this end we introduce
a novel approach, named ProgressNet, capable of predicting when an action takes
place in a video, where it is located within the frames, and how far it has
progressed during its execution. To provide a general definition of action
progress, we ground our work in the linguistics literature, borrowing terms and
concepts to understand which actions can be the subject of progress estimation.
As a result, we define a categorization of actions and their phases. Motivated
by the recent success obtained from the interaction of Convolutional and
Recurrent Neural Networks, our model is based on a combination of the Faster
R-CNN framework, to make frame-wise predictions, and LSTM networks, to estimate
action progress through time. After introducing two evaluation protocols for
the task at hand, we demonstrate the capability of our model to effectively
predict action progress on the UCF-101 and J-HMDB datasets
Video Object Detection with an Aligned Spatial-Temporal Memory
We introduce Spatial-Temporal Memory Networks for video object detection. At
its core, a novel Spatial-Temporal Memory module (STMM) serves as the recurrent
computation unit to model long-term temporal appearance and motion dynamics.
The STMM's design enables full integration of pretrained backbone CNN weights,
which we find to be critical for accurate detection. Furthermore, in order to
tackle object motion in videos, we propose a novel MatchTrans module to align
the spatial-temporal memory from frame to frame. Our method produces
state-of-the-art results on the benchmark ImageNet VID dataset, and our
ablative studies clearly demonstrate the contribution of our different design
choices. We release our code and models at
http://fanyix.cs.ucdavis.edu/project/stmn/project.html
Finding Action Tubes with a Sparse-to-Dense Framework
The task of spatial-temporal action detection has attracted increasing
attention among researchers. Existing dominant methods solve this problem by
relying on short-term information and dense serial-wise detection on each
individual frames or clips. Despite their effectiveness, these methods showed
inadequate use of long-term information and are prone to inefficiency. In this
paper, we propose for the first time, an efficient framework that generates
action tube proposals from video streams with a single forward pass in a
sparse-to-dense manner. There are two key characteristics in this framework:
(1) Both long-term and short-term sampled information are explicitly utilized
in our spatiotemporal network, (2) A new dynamic feature sampling module (DTS)
is designed to effectively approximate the tube output while keeping the system
tractable. We evaluate the efficacy of our model on the UCF101-24, JHMDB-21 and
UCFSports benchmark datasets, achieving promising results that are competitive
to state-of-the-art methods. The proposed sparse-to-dense strategy rendered our
framework about 7.6 times more efficient than the nearest competitor.Comment: 5 figures; AAAI 202
Understanding cities with machine eyes: A review of deep computer vision in urban analytics
Modelling urban systems has interested planners and modellers for decades. Different models have been achieved relying on mathematics, cellular automation, complexity, and scaling. While most of these models tend to be a simplification of reality, today within the paradigm shifts of artificial intelligence across the different fields of science, the applications of computer vision show promising potential in understanding the realistic dynamics of cities. While cities are complex by nature, computer vision shows progress in tackling a variety of complex physical and non-physical visual tasks. In this article, we review the tasks and algorithms of computer vision and their applications in understanding cities. We attempt to subdivide computer vision algorithms into tasks, and cities into layers to show evidence of where computer vision is intensively applied and where further research is needed. We focus on highlighting the potential role of computer vision in understanding urban systems related to the built environment, natural environment, human interaction, transportation, and infrastructure. After showing the diversity of computer vision algorithms and applications, the challenges that remain in understanding the integration between these different layers of cities and their interactions with one another relying on deep learning and computer vision. We also show recommendations for practice and policy-making towards reaching AI-generated urban policies
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