1,122 research outputs found
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
Pedestrian Attribute Recognition: A Survey
Recognizing pedestrian attributes is an important task in computer vision
community due to it plays an important role in video surveillance. Many
algorithms has been proposed to handle this task. The goal of this paper is to
review existing works using traditional methods or based on deep learning
networks. Firstly, we introduce the background of pedestrian attributes
recognition (PAR, for short), including the fundamental concepts of pedestrian
attributes and corresponding challenges. Secondly, we introduce existing
benchmarks, including popular datasets and evaluation criterion. Thirdly, we
analyse the concept of multi-task learning and multi-label learning, and also
explain the relations between these two learning algorithms and pedestrian
attribute recognition. We also review some popular network architectures which
have widely applied in the deep learning community. Fourthly, we analyse
popular solutions for this task, such as attributes group, part-based,
\emph{etc}. Fifthly, we shown some applications which takes pedestrian
attributes into consideration and achieve better performance. Finally, we
summarized this paper and give several possible research directions for
pedestrian attributes recognition. The project page of this paper can be found
from the following website:
\url{https://sites.google.com/view/ahu-pedestrianattributes/}.Comment: Check our project page for High Resolution version of this survey:
https://sites.google.com/view/ahu-pedestrianattributes
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