31,079 research outputs found
A Pose-Sensitive Embedding for Person Re-Identification with Expanded Cross Neighborhood Re-Ranking
Person re identification is a challenging retrieval task that requires
matching a person's acquired image across non overlapping camera views. In this
paper we propose an effective approach that incorporates both the fine and
coarse pose information of the person to learn a discriminative embedding. In
contrast to the recent direction of explicitly modeling body parts or
correcting for misalignment based on these, we show that a rather
straightforward inclusion of acquired camera view and/or the detected joint
locations into a convolutional neural network helps to learn a very effective
representation. To increase retrieval performance, re-ranking techniques based
on computed distances have recently gained much attention. We propose a new
unsupervised and automatic re-ranking framework that achieves state-of-the-art
re-ranking performance. We show that in contrast to the current
state-of-the-art re-ranking methods our approach does not require to compute
new rank lists for each image pair (e.g., based on reciprocal neighbors) and
performs well by using simple direct rank list based comparison or even by just
using the already computed euclidean distances between the images. We show that
both our learned representation and our re-ranking method achieve
state-of-the-art performance on a number of challenging surveillance image and
video datasets.
The code is available online at:
https://github.com/pse-ecn/pose-sensitive-embeddingComment: CVPR 2018: v2 (fixes, added new results on PRW dataset
Ability of head-mounted display technology to improve mobility in people with low vision: a systematic review
Purpose: The purpose of this study was to undertake a systematic literature review on how vision enhancements, implemented using head-mounted displays (HMDs), can improve mobility, orientation, and associated aspects of visual function in people with low vision.
Methods: The databases Medline, Chinl, Scopus, and Web of Science were searched for potentially relevant studies. Publications from all years until November 2018 were identified based on predefined inclusion and exclusion criteria. The data were tabulated and synthesized to produce a systematic review.
Results: The search identified 28 relevant papers describing the performance of vision enhancement techniques on mobility and associated visual tasks. Simplifying visual scenes improved obstacle detection and object recognition but decreased walking speed. Minification techniques increased the size of the visual field by 3 to 5 times and improved visual search performance. However, the impact of minification on mobility has not been studied extensively. Clinical trials with commercially available devices recorded poor results relative to conventional aids.
Conclusions: The effects of current vision enhancements using HMDs are mixed. They appear to reduce mobility efficiency but improved obstacle detection and object recognition. The review highlights the lack of controlled studies with robust study designs. To support the evidence base, well-designed trials with larger sample sizes that represent different types of impairments and real-life scenarios are required. Future work should focus on identifying the needs of people with different types of vision impairment and providing targeted enhancements.
Translational Relevance: This literature review examines the evidence regarding the ability of HMD technology to improve mobility in people with sight loss
CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap
After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in
multimedia search engines, we have identified and analyzed gaps within European research effort during our second year.
In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio-
economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown
of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on
requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the
community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our
Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as
National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core
technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research
challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal
challenges
Adaptive foveated single-pixel imaging with dynamic super-sampling
As an alternative to conventional multi-pixel cameras, single-pixel cameras
enable images to be recorded using a single detector that measures the
correlations between the scene and a set of patterns. However, to fully sample
a scene in this way requires at least the same number of correlation
measurements as there are pixels in the reconstructed image. Therefore
single-pixel imaging systems typically exhibit low frame-rates. To mitigate
this, a range of compressive sensing techniques have been developed which rely
on a priori knowledge of the scene to reconstruct images from an under-sampled
set of measurements. In this work we take a different approach and adopt a
strategy inspired by the foveated vision systems found in the animal kingdom -
a framework that exploits the spatio-temporal redundancy present in many
dynamic scenes. In our single-pixel imaging system a high-resolution foveal
region follows motion within the scene, but unlike a simple zoom, every frame
delivers new spatial information from across the entire field-of-view. Using
this approach we demonstrate a four-fold reduction in the time taken to record
the detail of rapidly evolving features, whilst simultaneously accumulating
detail of more slowly evolving regions over several consecutive frames. This
tiered super-sampling technique enables the reconstruction of video streams in
which both the resolution and the effective exposure-time spatially vary and
adapt dynamically in response to the evolution of the scene. The methods
described here can complement existing compressive sensing approaches and may
be applied to enhance a variety of computational imagers that rely on
sequential correlation measurements.Comment: 13 pages, 5 figure
- …