Pedestrian Detection
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Benchmarks
Caltech
CityPersons
LLVIP
DVTOD
TJU-Ped-traffic
TJU-Ped-campus
CVC14
MMPD-Dataset
Most implemented
YOLOv3: An Incremental Improvement
Focal Loss for Dense Object Detection
FCOS: Fully Convolutional One-Stage Object Detection
Feature Pyramid Networks for Object Detection
Papers
MV2GF: Multi-view Pedestrian Detection with a Visual Geometric Foundation Model
Multi-View Pedestrian Detection (MVPD) aims to detect pedestrians in the form of a bird's eye view map from multi-view images. Recent MVPD methods adopt a unified framework that projects 2D image features into a 3D world…
Pedestrian DetectionDescriptor: LYNRED Mobility Dataset Multimodal Detection Subset (LYNRED-MDS)
Current road safety systems primarily focus on minimizing post-collision damage. However, advances in algorithmic perception are shifting focus toward early collision prediction, especially in lowvisibility conditions li…
Pedestrian DetectionMVDGC: Joint 3D and 2D Multi-view Pedestrian Detection via Dual Geometric Constraints
The core challenge in multi-view pedestrian detection (MVPD) lies in effective aggregation of visual features from different viewpoints for robust occlusion reasoning. Recent approaches have addressed this by first proje…
Pedestrian DetectionFreqKD: Frequency-Decoupled Cross-Modal Knowledge Distillation for Infrared Object Detection
Transfer learning from large-scale RGB foundation models to infrared (IR) imagery through knowledge distillation (KD) remains challenging due to fundamental differences in image formation physics. We investigate the spec…
Knowledge DistillationPedestrian DetectionTransfer LearningObject DetectionMaking the Discrete Continuous: Synthetic RAW Augmentations for Fine-Grained Evaluation of Person Detection Performance in Low Light
Real-world deployment of AI vision models is both fueled and limited by the data available for training and testing. Real datasets are sparse and uneven: long-tailed or unbalanced distributions hinder generalization, and…
Pedestrian DetectionImage AugmentationAutonomous DrivingObject DetectionContrastive-SDXL: Annotation-Preserving Night-Time Augmentation for Pedestrian Detection
Night-time pedestrian detection remains challenging because labelled night-time data are limited and large illumination differences make daytime-only trained detectors unreliable. Latent diffusion models (LDMs) provide a…
Image-to-Image TranslationSemantic correspondencePedestrian Detection