184,581 research outputs found
Automated secure room system
Automated security systems are a useful addition for today's home where safety is essential. Vision-based security systems have the greater advantages over the traditional security systems such as using the lock, observing by the security guard, using alarm signal etc. This paper proposed an integrated dual-level vision-based home security system, which consists of two subsystems - a) movement detection and b) hand verification system. The primary movement detection technique is used to detect any movement first and the system verifies the authorized person for any secured place. It will check the threshold value where if the threshold level exceeds and the verification flag is off, the alarm will be triggered. Otherwise, if verification flag is on, it means the person is authorized and movement detection will be turned off for this person. On an event of a failure in the primary system, the secondary hand geometry verification module can act as a reliable backup to detect authorized person in a restricted area. Several experiment results have shown good performance and feasible implementation in both cases
Analysis of Hand Segmentation in the Wild
A large number of works in egocentric vision have concentrated on action and
object recognition. Detection and segmentation of hands in first-person videos,
however, has less been explored. For many applications in this domain, it is
necessary to accurately segment not only hands of the camera wearer but also
the hands of others with whom he is interacting. Here, we take an in-depth look
at the hand segmentation problem. In the quest for robust hand segmentation
methods, we evaluated the performance of the state of the art semantic
segmentation methods, off the shelf and fine-tuned, on existing datasets. We
fine-tune RefineNet, a leading semantic segmentation method, for hand
segmentation and find that it does much better than the best contenders.
Existing hand segmentation datasets are collected in the laboratory settings.
To overcome this limitation, we contribute by collecting two new datasets: a)
EgoYouTubeHands including egocentric videos containing hands in the wild, and
b) HandOverFace to analyze the performance of our models in presence of similar
appearance occlusions. We further explore whether conditional random fields can
help refine generated hand segmentations. To demonstrate the benefit of
accurate hand maps, we train a CNN for hand-based activity recognition and
achieve higher accuracy when a CNN was trained using hand maps produced by the
fine-tuned RefineNet. Finally, we annotate a subset of the EgoHands dataset for
fine-grained action recognition and show that an accuracy of 58.6% can be
achieved by just looking at a single hand pose which is much better than the
chance level (12.5%).Comment: Accepted at CVPR 201
A Unified Model for Tracking and Image-Video Detection Has More Power
Objection detection (OD) has been one of the most fundamental tasks in
computer vision. Recent developments in deep learning have pushed the
performance of image OD to new heights by learning-based, data-driven
approaches. On the other hand, video OD remains less explored, mostly due to
much more expensive data annotation needs. At the same time, multi-object
tracking (MOT) which requires reasoning about track identities and
spatio-temporal trajectories, shares similar spirits with video OD. However,
most MOT datasets are class-specific (e.g., person-annotated only), which
constrains a model's flexibility to perform tracking on other objects. We
propose TrIVD (Tracking and Image-Video Detection), the first framework that
unifies image OD, video OD, and MOT within one end-to-end model. To handle the
discrepancies and semantic overlaps across datasets, TrIVD formulates
detection/tracking as grounding and reasons about object categories via
visual-text alignments. The unified formulation enables cross-dataset,
multi-task training, and thus equips TrIVD with the ability to leverage
frame-level features, video-level spatio-temporal relations, as well as track
identity associations. With such joint training, we can now extend the
knowledge from OD data, that comes with much richer object category
annotations, to MOT and achieve zero-shot tracking capability. Experiments
demonstrate that TrIVD achieves state-of-the-art performances across all
image/video OD and MOT tasks.Comment: (13 pages, 4 figures
The Evolution of First Person Vision Methods: A Survey
The emergence of new wearable technologies such as action cameras and
smart-glasses has increased the interest of computer vision scientists in the
First Person perspective. Nowadays, this field is attracting attention and
investments of companies aiming to develop commercial devices with First Person
Vision recording capabilities. Due to this interest, an increasing demand of
methods to process these videos, possibly in real-time, is expected. Current
approaches present a particular combinations of different image features and
quantitative methods to accomplish specific objectives like object detection,
activity recognition, user machine interaction and so on. This paper summarizes
the evolution of the state of the art in First Person Vision video analysis
between 1997 and 2014, highlighting, among others, most commonly used features,
methods, challenges and opportunities within the field.Comment: First Person Vision, Egocentric Vision, Wearable Devices, Smart
Glasses, Computer Vision, Video Analytics, Human-machine Interactio
Towards Vision-Based Smart Hospitals: A System for Tracking and Monitoring Hand Hygiene Compliance
One in twenty-five patients admitted to a hospital will suffer from a
hospital acquired infection. If we can intelligently track healthcare staff,
patients, and visitors, we can better understand the sources of such
infections. We envision a smart hospital capable of increasing operational
efficiency and improving patient care with less spending. In this paper, we
propose a non-intrusive vision-based system for tracking people's activity in
hospitals. We evaluate our method for the problem of measuring hand hygiene
compliance. Empirically, our method outperforms existing solutions such as
proximity-based techniques and covert in-person observational studies. We
present intuitive, qualitative results that analyze human movement patterns and
conduct spatial analytics which convey our method's interpretability. This work
is a step towards a computer-vision based smart hospital and demonstrates
promising results for reducing hospital acquired infections.Comment: Machine Learning for Healthcare Conference (MLHC
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