10,876 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
Proceedings of the 2021 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory
2021, the annual joint workshop of the Fraunhofer IOSB and KIT IES was hosted at the IOSB in Karlsruhe. For a week from the 2nd to the 6th July the doctoral students extensive reports on the status of their research. The results and ideas presented at the workshop are collected in this book in the form of detailed technical reports
Proceedings of the 2021 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory
2021, the annual joint workshop of the Fraunhofer IOSB and KIT IES was hosted at the IOSB in Karlsruhe. For a week from the 2nd to the 6th July the doctoral students extensive reports on the status of their research. The results and ideas presented at the workshop are collected in this book in the form of detailed technical reports
DiP: Learning Discriminative Implicit Parts for Person Re-Identification
In person re-identification (ReID) tasks, many works explore the learning of
part features to improve the performance over global image features. Existing
methods extract part features in an explicit manner, by either using a
hand-designed image division or keypoints obtained with external visual
systems. In this work, we propose to learn Discriminative implicit Parts (DiPs)
which are decoupled from explicit body parts. Therefore, DiPs can learn to
extract any discriminative features that can benefit in distinguishing
identities, which is beyond predefined body parts (such as accessories).
Moreover, we propose a novel implicit position to give a geometric
interpretation for each DiP. The implicit position can also serve as a learning
signal to encourage DiPs to be more position-equivariant with the identity in
the image. Lastly, a set of attributes and auxiliary losses are introduced to
further improve the learning of DiPs. Extensive experiments show that the
proposed method achieves state-of-the-art performance on multiple person ReID
benchmarks
Dynamic Prototype Mask for Occluded Person Re-Identification
Although person re-identification has achieved an impressive improvement in
recent years, the common occlusion case caused by different obstacles is still
an unsettled issue in real application scenarios. Existing methods mainly
address this issue by employing body clues provided by an extra network to
distinguish the visible part. Nevertheless, the inevitable domain gap between
the assistant model and the ReID datasets has highly increased the difficulty
to obtain an effective and efficient model. To escape from the extra
pre-trained networks and achieve an automatic alignment in an end-to-end
trainable network, we propose a novel Dynamic Prototype Mask (DPM) based on two
self-evident prior knowledge. Specifically, we first devise a Hierarchical Mask
Generator which utilizes the hierarchical semantic to select the visible
pattern space between the high-quality holistic prototype and the feature
representation of the occluded input image. Under this condition, the occluded
representation could be well aligned in a selected subspace spontaneously.
Then, to enrich the feature representation of the high-quality holistic
prototype and provide a more complete feature space, we introduce a Head Enrich
Module to encourage different heads to aggregate different patterns
representation in the whole image. Extensive experimental evaluations conducted
on occluded and holistic person re-identification benchmarks demonstrate the
superior performance of the DPM over the state-of-the-art methods. The code is
released at https://github.com/stone96123/DPM.Comment: Accepted by ACM MM 202
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