94 research outputs found
A Multiple Component Matching Framework for Person Re-Identification
Person re-identification consists in recognizing an individual that has
already been observed over a network of cameras. It is a novel and challenging
research topic in computer vision, for which no reference framework exists yet.
Despite this, previous works share similar representations of human body based
on part decomposition and the implicit concept of multiple instances. Building
on these similarities, we propose a Multiple Component Matching (MCM) framework
for the person re-identification problem, which is inspired by Multiple
Component Learning, a framework recently proposed for object detection. We show
that previous techniques for person re-identification can be considered
particular implementations of our MCM framework. We then present a novel person
re-identification technique as a direct, simple implementation of our
framework, focused in particular on robustness to varying lighting conditions,
and show that it can attain state of the art performances.Comment: Accepted paper, 16th Int. Conf. on Image Analysis and Processing
(ICIAP 2011), Ravenna, Italy, 14/09/201
Fitting 3D Morphable Models using Local Features
In this paper, we propose a novel fitting method that uses local image
features to fit a 3D Morphable Model to 2D images. To overcome the obstacle of
optimising a cost function that contains a non-differentiable feature
extraction operator, we use a learning-based cascaded regression method that
learns the gradient direction from data. The method allows to simultaneously
solve for shape and pose parameters. Our method is thoroughly evaluated on
Morphable Model generated data and first results on real data are presented.
Compared to traditional fitting methods, which use simple raw features like
pixel colour or edge maps, local features have been shown to be much more
robust against variations in imaging conditions. Our approach is unique in that
we are the first to use local features to fit a Morphable Model.
Because of the speed of our method, it is applicable for realtime
applications. Our cascaded regression framework is available as an open source
library (https://github.com/patrikhuber).Comment: Submitted to ICIP 2015; 4 pages, 4 figure
Development of a smart post-hospitalization facility for older people by using domotics, robotics, and automated tele-monitoring
Recent studies showed that about the 8% of beds are occupied by patients who experience a delayed hospital discharge (DHD). This is attributed to a delay in the arrangement of home-care assistance or in admission to long-term care facilities. Recently a lot of technologies have been developed to improve caring and monitoring of older people. The aim of this study is to design, implement and test a prototype of a technology based post-hospitalization facility for older people at risk of DHD by using domotics, robotics and wearable sensors for tele-monitoring. A sensorised posthospitalization facility has been built inside the hospital. Thirty-five healthy volunteers aged from 20 to 82 years were recruited. Clinical and functional assessment, i.e. motility index (MI), and human-robot interaction satisfaction were measured. A significant correlation was observed between automatic MI and the Gait Speed, the time sit-to-stand, and the Timed Up and Go test. Domotics, robotics and technology-based telemonitoring may represent a new way to assess patient’s autonomy and functional and clinical conditions in an ecological way, reproducing as much as possible a real life at home
Alignment-based Similarity of People Trajectories using Semi-directional Statistics
This paper presents a method for comparing people trajectories for video surveillance applications, based on semi-directional statistics. In fact, the modelling of a trajectory as a sequence of angles, speeds and time lags, requires the use of a statistical tool capable to jointly consider periodic and linear variables. Our statistical method is compared with two state-of-the-art methods
SimSwap: An Efficient Framework For High Fidelity Face Swapping
We propose an efficient framework, called Simple Swap (SimSwap), aiming for
generalized and high fidelity face swapping. In contrast to previous approaches
that either lack the ability to generalize to arbitrary identity or fail to
preserve attributes like facial expression and gaze direction, our framework is
capable of transferring the identity of an arbitrary source face into an
arbitrary target face while preserving the attributes of the target face. We
overcome the above defects in the following two ways. First, we present the ID
Injection Module (IIM) which transfers the identity information of the source
face into the target face at feature level. By using this module, we extend the
architecture of an identity-specific face swapping algorithm to a framework for
arbitrary face swapping. Second, we propose the Weak Feature Matching Loss
which efficiently helps our framework to preserve the facial attributes in an
implicit way. Extensive experiments on wild faces demonstrate that our SimSwap
is able to achieve competitive identity performance while preserving attributes
better than previous state-of-the-art methods. The code is already available on
github: https://github.com/neuralchen/SimSwap.Comment: Accepted by ACMMM 202
Noise Reduction for CFA Image Sensors Exploiting HVS Behaviour
This paper presents a spatial noise reduction technique designed to work on CFA (Color Filtering Array) data acquired by CCD/CMOS image sensors. The overall processing preserves image details using some heuristics related to the HVS (Human Visual System); estimates of local texture degree and noise levels are computed to regulate the filter smoothing capability. Experimental results confirm the effectiveness of the proposed technique. The method is also suitable for implementation in low power mobile devices with imaging capabilities such as camera phones and PDAs
Side-information generation for temporally and spatially scalablewyner-ziv codecs
The distributed video coding paradigmenables video codecs to operate with reversed complexity, in which the complexity is shifted from the encoder toward the decoder. Its performance is heavily dependent on the quality of the side information generated by motio estimation at the decoder. We compare the rate-distortion performance of different side-information estimators, for both temporally and spatially scalableWyner-Ziv codecs. For the temporally scalable codec we compared an established method with a new algorithm that uses a linear-motion model to produce side-information. As a continuation of previous works, in this paper, we propose to use a super-resolution method to upsample the nonkey frame, for the spatial scalable codec, using the key frames as reference.We verify the performance of the spatial scalableWZcoding using the state-of-the-art video coding standard H.264/AVC
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