Pedestrian Attribute Recognition and Person Re-Identification Call for Papers

Special Issue on Pedestrian Attribute Recognition and Person Re-Identification
Pattern Analysis and Applications
Website: https://link.springer.com/collections/ifbjhfcbbh

Submission Deadline: 31 January 2025

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=== Call for papers ===

Pedestrian attributes recognition and person re-identification from
images and videos is nowadays a relevant problem in several real
applications, such as forensics, digital signage, social robotics,
business intelligence, people tracking and multi-camera person
re-identification.

In terms of neural network architecture, there is still a limited use
of visual attention mechanisms, which could allow for more accurate
and efficient part localization and recognition; furthermore, recent
advanced fully convolutional network architectures or based on
transformers could be explored. Newly designed loss functions could be
adopted to deal with unbalanced data, while multi-task learning
approaches may represent an excellent solution to exploit the
interdependencies between the attributes while maintaining the
processing time unchanged as the number of attributes increases.

The efficiency is another aspect that is important to consider, since
fast person re-identification is crucial for people tracking and
multi-camera person re-identification in crowded scenarios such
stations or airports; to this aim, in addition to multi-task
architectures, end-to-end solutions for people detection,
re-identification and tracking would be of great interest to the
scientific community.

The robustness of these methods should be investigated in various
background, resolution, and illumination conditions, considering that
these variations can interfere with the recognition of pedestrian
attributes and affect the performance of person re-identification;
datasets and benchmark soliciting the algorithms in the wild would be
a significant contribution to this aim. To achieve this robustness,
multi-frame or multi-modal inputs may be considered; video-based or
multi-sensor (RGB camera, thermal camera, depth camera, LIDAR)
pedestrian attribute recognition and person re-identification
approaches could exploit the additional information to improve
accuracy and robustness of real applications. Finally, it is worth
mentioning that the very recent Pedestrian Attribute Recognition
contest (PAR 2023) was won by a method based on Visual Question
Answering; this surprising and impressive result suggests that the
investigation of this type of methods or other foundation models can
be a very promising line of research in this field.

Considering the relevance of the topic for the possible exploitation
in real applications and all the above-mentioned possible directions
of research and improvements of existing algorithms, the special issue
has the goal of collecting innovative scientific papers to advance the
state of the art in the recognition of pedestrian attributes and
person re-identification. The topic and the innovative contributions
in this field are aligned with the aims and scope of the journal and,
thus, can be relevant for its audience and readership.

Topics of interest of the proposed Special Issue, related to
pedestrian attribute recognition and person re-identification, are but
not limited to:

* Machine learning algorithms for human attribute recognition
* Deep learning models for people detection and classification
* Appearance based people tracking
* Multi-camera people re-identification
* Multi-frame person retrieval
* Multi-modal people detection and re-identification
* Visual question answering applied to human attributes
* New datasets and benchmark for pedestrian attribute recognition and person re-identification
* Novel applications and case studies in surveillance scenarios

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=== Guest editors ===

Modesto Castrillon-Santana (University of Las Palmas De Gran Canaria, Spain)
Antonio Greco (University of Salerno, Italy)
Nicolai Petkov (University of Groningen, The Netherlands)
Bruno Vento (University of Napoli Federico II, Italy)

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