26,513 research outputs found
Attribute Recognition by Joint Recurrent Learning of Context and Correlation
Recognising semantic pedestrian attributes in surveillance images is a
challenging task for computer vision, particularly when the imaging quality is
poor with complex background clutter and uncontrolled viewing conditions, and
the number of labelled training data is small. In this work, we formulate a
Joint Recurrent Learning (JRL) model for exploring attribute context and
correlation in order to improve attribute recognition given small sized
training data with poor quality images. The JRL model learns jointly pedestrian
attribute correlations in a pedestrian image and in particular their sequential
ordering dependencies (latent high-order correlation) in an end-to-end
encoder/decoder recurrent network. We demonstrate the performance advantage and
robustness of the JRL model over a wide range of state-of-the-art deep models
for pedestrian attribute recognition, multi-label image classification, and
multi-person image annotation on two largest pedestrian attribute benchmarks
PETA and RAP.Comment: Accepted by ICCV 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
- …