Pedestrian Detection
113 papers with code • 6 benchmarks • 15 datasets
Pedestrian detection is the task of detecting pedestrians from a camera.
Further state-of-the-art results (e.g. on the KITTI dataset) can be found at 3D Object Detection.
( Image credit: High-level Semantic Feature Detection: A New Perspective for Pedestrian Detection )
Benchmarks
These leaderboards are used to track progress in Pedestrian Detection
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Libraries
Use these libraries to find Pedestrian Detection models and implementationsDatasets
CityPersons
PRW
ETH
LLVIP
INRIA Person
TJU-DHD
CADP
EuroCity Persons
RailEye3D Dataset
Latest papers
Learning Scene-Pedestrian Graph for End to end Person Search
Vill-Lab/2023-TII-SPG
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IEEE Transactions on Industrial Informatics 2023
In this article, a novel scene-pedestrian graph (SPG) is proposed, which can explicitly model the interplay between the pedestrians and scenes.
Continual Learning for Out-of-Distribution Pedestrian Detection
mahdiyarmm/continual-pedestrian-detection
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A continual learning solution is proposed to address the out-of-distribution generalization problem for pedestrian detection.
TFDet: Target-Aware Fusion for RGB-T Pedestrian Detection
xuez-phd/tfdet
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In this paper, we propose a novel target-aware fusion strategy for multispectral pedestrian detection, named TFDet.
Pedestrian Behavior Maps for Safety Advisories: CHAMP Framework and Real-World Data Analysis
It is critical for vehicles to prevent any collisions with pedestrians.
CARLA-BSP: a simulated dataset with pedestrians
We present a sample dataset featuring pedestrians generated using the ARCANE framework, a new framework for generating datasets in CARLA (0. 9. 13).
A Preliminary Study of Deep Learning Sensor Fusion for Pedestrian Detection
Additionally, a custom dataset of 60 images was proposed for training the architecture, with an additional 10 for evaluation and 10 for testing, giving a total of 80 images.
VLPD: Context-Aware Pedestrian Detection via Vision-Language Semantic Self-Supervision
Firstly, we propose a self-supervised Vision-Language Semantic (VLS) segmentation method, which learns both fully-supervised pedestrian detection and contextual segmentation via self-generated explicit labels of semantic classes by vision-language models.
Beyond Appearance: a Semantic Controllable Self-Supervised Learning Framework for Human-Centric Visual Tasks
modelscope/modelscope
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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.
HumanBench: Towards General Human-centric Perception with Projector Assisted Pretraining
opengvlab/humanbench
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CVPR 2023
Specifically, we propose a \textbf{HumanBench} based on existing datasets to comprehensively evaluate on the common ground the generalization abilities of different pretraining methods on 19 datasets from 6 diverse downstream tasks, including person ReID, pose estimation, human parsing, pedestrian attribute recognition, pedestrian detection, and crowd counting.
UniHCP: A Unified Model for Human-Centric Perceptions
opengvlab/unihcp
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CVPR 2023
When adapted to a specific task, UniHCP achieves new SOTAs on a wide range of human-centric tasks, e. g., 69. 8 mIoU on CIHP for human parsing, 86. 18 mA on PA-100K for attribute prediction, 90. 3 mAP on Market1501 for ReID, and 85. 8 JI on CrowdHuman for pedestrian detection, performing better than specialized models tailored for each task.
