Pedestrian Attribute Recognition
31 papers with code • 5 benchmarks • 5 datasets
Pedestrian attribution recognition is the task of recognizing pedestrian features - such as whether they are talking on a phone, whether they have a backpack, and so on.
( Image credit: HydraPlus-Net: Attentive Deep Features for Pedestrian Analysis )
Benchmarks
These leaderboards are used to track progress in Pedestrian Attribute Recognition
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Libraries
Use these libraries to find Pedestrian Attribute Recognition models and implementations
PETA
PA-100K
UAV-Human
RAP
CAR
Most implemented papers
Densely Connected Convolutional Networks
liuzhuang13/DenseNet
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CVPR 2017
Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output.
Beyond Appearance: a Semantic Controllable Self-Supervised Learning Framework for Human-Centric Visual Tasks
tinyvision/SOLIDER
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CVPR 2023
Unlike the existing self-supervised learning methods, prior knowledge from human images is utilized in SOLIDER to build pseudo semantic labels and import more semantic information into the learned representation.
A Richly Annotated Dataset for Pedestrian Attribute Recognition
valencebond/Rethinking_of_PAR
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RAP has in total 41, 585 pedestrian samples, each of which is annotated with 72 attributes as well as viewpoints, occlusions, body parts information.
Spatio-Temporal Side Tuning Pre-trained Foundation Models for Video-based Pedestrian Attribute Recognition
wangxiao5791509/Pedestrian-Attribute-Recognition-Paper-List
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Specifically, we formulate the video-based PAR as a vision-language fusion problem and adopt a pre-trained foundation model CLIP to extract the visual features.
HydraPlus-Net: Attentive Deep Features for Pedestrian Analysis
Pedestrian analysis plays a vital role in intelligent video surveillance and is a key component for security-centric computer vision systems.
Rethinking of Pedestrian Attribute Recognition: Realistic Datasets with Efficient Method
valencebond/Rethinking_of_PAR
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Despite various methods are proposed to make progress in pedestrian attribute recognition, a crucial problem on existing datasets is often neglected, namely, a large number of identical pedestrian identities in train and test set, which is not consistent with practical application.
UAV-Human: A Large Benchmark for Human Behavior Understanding with Unmanned Aerial Vehicles
SUTDCV/UAV-Human
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CVPR 2021
Human behavior understanding with unmanned aerial vehicles (UAVs) is of great significance for a wide range of applications, which simultaneously brings an urgent demand of large, challenging, and comprehensive benchmarks for the development and evaluation of UAV-based models.
YinYang-Net: Complementing Face and Body Information for Wild Gender Recognition
To overcome these limitations, we: 1) present frontal and wild face versions of three well-known surveillance datasets; and 2) propose YinYang-Net (YY-Net), a model that effectively and dynamically complements facial and body information, which makes it suitable for gender recognition in wild conditions.
A Solution to Co-occurrence Bias: Attributes Disentanglement via Mutual Information Minimization for Pedestrian Attribute Recognition
sdret/a-solution-to-co-occurence-bias-in-pedestrian-attribute-recognition
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Recent studies on pedestrian attribute recognition progress with either explicit or implicit modeling of the co-occurrence among attributes.
Hulk: A Universal Knowledge Translator for Human-Centric Tasks
Human-centric perception tasks, e. g., pedestrian detection, skeleton-based action recognition, and pose estimation, have wide industrial applications, such as metaverse and sports analysis.
