2,143 research outputs found
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
Learning Deep Context-aware Features over Body and Latent Parts for Person Re-identification
Person Re-identification (ReID) is to identify the same person across
different cameras. It is a challenging task due to the large variations in
person pose, occlusion, background clutter, etc How to extract powerful
features is a fundamental problem in ReID and is still an open problem today.
In this paper, we design a Multi-Scale Context-Aware Network (MSCAN) to learn
powerful features over full body and body parts, which can well capture the
local context knowledge by stacking multi-scale convolutions in each layer.
Moreover, instead of using predefined rigid parts, we propose to learn and
localize deformable pedestrian parts using Spatial Transformer Networks (STN)
with novel spatial constraints. The learned body parts can release some
difficulties, eg pose variations and background clutters, in part-based
representation. Finally, we integrate the representation learning processes of
full body and body parts into a unified framework for person ReID through
multi-class person identification tasks. Extensive evaluations on current
challenging large-scale person ReID datasets, including the image-based
Market1501, CUHK03 and sequence-based MARS datasets, show that the proposed
method achieves the state-of-the-art results.Comment: Accepted by CVPR 201
Improving Pedestrian Attribute Recognition With Weakly-Supervised Multi-Scale Attribute-Specific Localization
Pedestrian attribute recognition has been an emerging research topic in the
area of video surveillance. To predict the existence of a particular attribute,
it is demanded to localize the regions related to the attribute. However, in
this task, the region annotations are not available. How to carve out these
attribute-related regions remains challenging. Existing methods applied
attribute-agnostic visual attention or heuristic body-part localization
mechanisms to enhance the local feature representations, while neglecting to
employ attributes to define local feature areas. We propose a flexible
Attribute Localization Module (ALM) to adaptively discover the most
discriminative regions and learns the regional features for each attribute at
multiple levels. Moreover, a feature pyramid architecture is also introduced to
enhance the attribute-specific localization at low-levels with high-level
semantic guidance. The proposed framework does not require additional region
annotations and can be trained end-to-end with multi-level deep supervision.
Extensive experiments show that the proposed method achieves state-of-the-art
results on three pedestrian attribute datasets, including PETA, RAP, and
PA-100K.Comment: Accepted by ICCV 201
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