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 with no code
Enhanced Cooperative Perception for Autonomous Vehicles Using Imperfect Communication
To validate our approach, we used the CARLA simulator to create a dataset of annotated videos for different driving scenarios where pedestrian detection is challenging for an AV with compromised vision.
PathFinder: Attention-Driven Dynamic Non-Line-of-Sight Tracking with a Mobile Robot
The study of non-line-of-sight (NLOS) imaging is growing due to its many potential applications, including rescue operations and pedestrian detection by self-driving cars.
MSCoTDet: Language-driven Multi-modal Fusion for Improved Multispectral Pedestrian Detection
Specifically, we generate text descriptions of the pedestrian in each RGB and thermal modality and design a Multispectral Chain-of-Thought (MSCoT) prompting, which models a step-by-step process to facilitate cross-modal reasoning at the semantic level and perform accurate detection.
Data Augmentation in Human-Centric Vision
This survey presents a comprehensive analysis of data augmentation techniques in human-centric vision tasks, a first of its kind in the field.
A Survey of Vision Transformers in Autonomous Driving: Current Trends and Future Directions
This survey explores the adaptation of visual transformer models in Autonomous Driving, a transition inspired by their success in Natural Language Processing.
A Flow-based Credibility Metric for Safety-critical Pedestrian Detection
Safety is of utmost importance for perception in automated driving (AD).
A Safety-Adapted Loss for Pedestrian Detection in Automated Driving
As common evaluation metrics are not an adequate safety indicator, recent works employ approaches to identify safety-critical VRU and back-annotate the risk to the object detector.
Pedestrian Detection in Low-Light Conditions: A Comprehensive Survey
Pedestrian detection remains a critical problem in various domains, such as computer vision, surveillance, and autonomous driving.
Generation of Synthetic Images for Pedestrian Detection Using a Sequence of GANs
Creating annotated datasets demands a substantial amount of manual effort.
Enhancing Multi-View Pedestrian Detection Through Generalized 3D Feature Pulling
The main challenge in multi-view pedestrian detection is integrating view-specific features into a unified space for comprehensive end-to-end perception.
