19,862 research outputs found
Gait recognition and understanding based on hierarchical temporal memory using 3D gait semantic folding
Gait recognition and understanding systems have shown a wide-ranging application prospect. However, their use of unstructured data from image and video has affected their performance, e.g., they are easily influenced by multi-views, occlusion, clothes, and object carrying conditions. This paper addresses these problems using a realistic 3-dimensional (3D) human structural data and sequential pattern learning framework with top-down attention modulating mechanism based on Hierarchical Temporal Memory (HTM). First, an accurate 2-dimensional (2D) to 3D human body pose and shape semantic parameters estimation method is proposed, which exploits the advantages of an instance-level body parsing model and a virtual dressing method. Second, by using gait semantic folding, the estimated body parameters are encoded using a sparse 2D matrix to construct the structural gait semantic image. In order to achieve time-based gait recognition, an HTM Network is constructed to obtain the sequence-level gait sparse distribution representations (SL-GSDRs). A top-down attention mechanism is introduced to deal with various conditions including multi-views by refining the SL-GSDRs, according to prior knowledge. The proposed gait learning model not only aids gait recognition tasks to overcome the difficulties in real application scenarios but also provides the structured gait semantic images for visual cognition. Experimental analyses on CMU MoBo, CASIA B, TUM-IITKGP, and KY4D datasets show a significant performance gain in terms of accuracy and robustness
Cosmic cookery : making a stereoscopic 3D animated movie.
This paper describes our experience making a short stereoscopic movie visualizing the development of structure in
the universe during the 13.7 billion years from the Big Bang to the present day. Aimed at a general audience for
the Royal Society's 2005 Summer Science Exhibition, the movie illustrates how the latest cosmological theories
based on dark matter and dark energy are capable of producing structures as complex as spiral galaxies and
allows the viewer to directly compare observations from the real universe with theoretical results. 3D is an
inherent feature of the cosmology data sets and stereoscopic visualization provides a natural way to present the
images to the viewer, in addition to allowing researchers to visualize these vast, complex data sets.
The presentation of the movie used passive, linearly polarized projection onto a 2m wide screen but it was
also required to playback on a Sharp RD3D display and in anaglyph projection at venues without dedicated
stereoscopic display equipment. Additionally lenticular prints were made from key images in the movie. We
discuss the following technical challenges during the stereoscopic production process; 1) Controlling the depth
presentation, 2) Editing the stereoscopic sequences, 3) Generating compressed movies in display speci¯c formats.
We conclude that the generation of high quality stereoscopic movie content using desktop tools and equipment
is feasible. This does require careful quality control and manual intervention but we believe these overheads
are worthwhile when presenting inherently 3D data as the result is signi¯cantly increased impact and better
understanding of complex 3D scenes
A Deep-structured Conditional Random Field Model for Object Silhouette Tracking
In this work, we introduce a deep-structured conditional random field
(DS-CRF) model for the purpose of state-based object silhouette tracking. The
proposed DS-CRF model consists of a series of state layers, where each state
layer spatially characterizes the object silhouette at a particular point in
time. The interactions between adjacent state layers are established by
inter-layer connectivity dynamically determined based on inter-frame optical
flow. By incorporate both spatial and temporal context in a dynamic fashion
within such a deep-structured probabilistic graphical model, the proposed
DS-CRF model allows us to develop a framework that can accurately and
efficiently track object silhouettes that can change greatly over time, as well
as under different situations such as occlusion and multiple targets within the
scene. Experiment results using video surveillance datasets containing
different scenarios such as occlusion and multiple targets showed that the
proposed DS-CRF approach provides strong object silhouette tracking performance
when compared to baseline methods such as mean-shift tracking, as well as
state-of-the-art methods such as context tracking and boosted particle
filtering.Comment: 17 page
Long-Term Visual Object Tracking Benchmark
We propose a new long video dataset (called Track Long and Prosper - TLP) and
benchmark for single object tracking. The dataset consists of 50 HD videos from
real world scenarios, encompassing a duration of over 400 minutes (676K
frames), making it more than 20 folds larger in average duration per sequence
and more than 8 folds larger in terms of total covered duration, as compared to
existing generic datasets for visual tracking. The proposed dataset paves a way
to suitably assess long term tracking performance and train better deep
learning architectures (avoiding/reducing augmentation, which may not reflect
real world behaviour). We benchmark the dataset on 17 state of the art trackers
and rank them according to tracking accuracy and run time speeds. We further
present thorough qualitative and quantitative evaluation highlighting the
importance of long term aspect of tracking. Our most interesting observations
are (a) existing short sequence benchmarks fail to bring out the inherent
differences in tracking algorithms which widen up while tracking on long
sequences and (b) the accuracy of trackers abruptly drops on challenging long
sequences, suggesting the potential need of research efforts in the direction
of long-term tracking.Comment: ACCV 2018 (Oral
Calipso: Physics-based Image and Video Editing through CAD Model Proxies
We present Calipso, an interactive method for editing images and videos in a
physically-coherent manner. Our main idea is to realize physics-based
manipulations by running a full physics simulation on proxy geometries given by
non-rigidly aligned CAD models. Running these simulations allows us to apply
new, unseen forces to move or deform selected objects, change physical
parameters such as mass or elasticity, or even add entire new objects that
interact with the rest of the underlying scene. In Calipso, the user makes
edits directly in 3D; these edits are processed by the simulation and then
transfered to the target 2D content using shape-to-image correspondences in a
photo-realistic rendering process. To align the CAD models, we introduce an
efficient CAD-to-image alignment procedure that jointly minimizes for rigid and
non-rigid alignment while preserving the high-level structure of the input
shape. Moreover, the user can choose to exploit image flow to estimate scene
motion, producing coherent physical behavior with ambient dynamics. We
demonstrate Calipso's physics-based editing on a wide range of examples
producing myriad physical behavior while preserving geometric and visual
consistency.Comment: 11 page
MobiFace: A Novel Dataset for Mobile Face Tracking in the Wild
Face tracking serves as the crucial initial step in mobile applications
trying to analyse target faces over time in mobile settings. However, this
problem has received little attention, mainly due to the scarcity of dedicated
face tracking benchmarks. In this work, we introduce MobiFace, the first
dataset for single face tracking in mobile situations. It consists of 80
unedited live-streaming mobile videos captured by 70 different smartphone users
in fully unconstrained environments. Over bounding boxes are manually
labelled. The videos are carefully selected to cover typical smartphone usage.
The videos are also annotated with 14 attributes, including 6 newly proposed
attributes and 8 commonly seen in object tracking. 36 state-of-the-art
trackers, including facial landmark trackers, generic object trackers and
trackers that we have fine-tuned or improved, are evaluated. The results
suggest that mobile face tracking cannot be solved through existing approaches.
In addition, we show that fine-tuning on the MobiFace training data
significantly boosts the performance of deep learning-based trackers,
suggesting that MobiFace captures the unique characteristics of mobile face
tracking. Our goal is to offer the community a diverse dataset to enable the
design and evaluation of mobile face trackers. The dataset, annotations and the
evaluation server will be on \url{https://mobiface.github.io/}.Comment: To appear on The 14th IEEE International Conference on Automatic Face
and Gesture Recognition (FG 2019
- …