SOTAVerified

Pedestrian Detection

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 )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 51–100 of 438 papers

TitleStatusHype
Exploring Visual Context for Weakly Supervised Person SearchCode1
NMS-Loss: Learning with Non-Maximum Suppression for Crowded Pedestrian DetectionCode1
Incremental Multi-Target Domain Adaptation for Object Detection with Efficient Domain TransferCode1
Multimodal Object Detection via Probabilistic EnsemblingCode1
Anchor-Free Person SearchCode1
RGB-D Railway Platform Monitoring and Scene Understanding for Enhanced Passenger SafetyCode1
LLA: Loss-aware Label Assignment for Dense Pedestrian DetectionCode1
Guided Attentive Feature Fusion for Multispectral Pedestrian DetectionCode1
Body-Face Joint Detection via Embedding and Head HookCode1
DETR for Crowd Pedestrian DetectionCode1
TJU-DHD: A Diverse High-Resolution Dataset for Object DetectionCode1
From Handcrafted to Deep Features for Pedestrian Detection: A SurveyCode1
Anchor-free Small-scale Multispectral Pedestrian DetectionCode1
Improving Multispectral Pedestrian Detection by Addressing Modality Imbalance ProblemsCode1
Task-conditioned Domain Adaptation for Pedestrian Detection in Thermal ImageryCode1
NOH-NMS: Improving Pedestrian Detection by Nearby Objects HallucinationCode1
Multiview Detection with Feature Perspective TransformationCode1
Occluded Prohibited Items Detection: an X-ray Security Inspection Benchmark and De-occlusion Attention ModuleCode1
Adapted Center and Scale Prediction: More Stable and More AccurateCode1
Center and Scale Prediction: Anchor-free Approach for Pedestrian and Face DetectionCode1
FCOS: Fully Convolutional One-Stage Object DetectionCode1
CrowdHuman: A Benchmark for Detecting Human in a CrowdCode1
YOLOv3: An Incremental ImprovementCode1
CityPersons: A Diverse Dataset for Pedestrian DetectionCode1
Multispectral Deep Neural Networks for Pedestrian DetectionCode1
YOLO-APD: Enhancing YOLOv8 for Robust Pedestrian Detection on Complex Road Geometries—0
Distance Estimation in Outdoor Driving Environments Using Phase-only Correlation Method with Event Cameras—0
Attention-Aware Multi-View Pedestrian Tracking—0
Panoramic Distortion-Aware Tokenization for Person Detection and Localization Using Transformers in Overhead Fisheye Images—0
Enhanced Multi-View Pedestrian Detection Using Probabilistic Occupancy Volume—0
Adversarial Attacks on Event-Based Pedestrian Detectors: A Physical Approach—0
PFSD: A Multi-Modal Pedestrian-Focus Scene Dataset for Rich Tasks in Semi-Structured EnvironmentsCode0
PedDet: Adaptive Spectral Optimization for Multimodal Pedestrian DetectionCode0
A Privacy Enhancing Technique to Evade Detection by Street Video Cameras Without Using Adversarial Accessories—0
Integrating LLMs with ITS: Recent Advances, Potentials, Challenges, and Future Directions—0
Multi-View Pedestrian Occupancy Prediction with a Novel Synthetic Dataset—0
Prediction of Occluded Pedestrians in Road Scenes using Human-like Reasoning: Insights from the OccluRoads Dataset—0
MVUDA: Unsupervised Domain Adaptation for Multi-view Pedestrian DetectionCode0
Revisiting Misalignment in Multispectral Pedestrian Detection: A Language-Driven Approach for Cross-modal Alignment Fusion—0
Hybrid Attention for Robust RGB-T Pedestrian Detection in Real-World Conditions—0
CAFF-DINO: Multi-spectral object detection transformers with cross-attention features fusion—0
GSON: A Group-based Social Navigation Framework with Large Multimodal Model—0
Real-Time Pedestrian Detection on IoT Edge Devices: A Lightweight Deep Learning Approach—0
Mean Height Aided Post-Processing for Pedestrian Detection—0
Video-to-Text Pedestrian Monitoring (VTPM): Leveraging Computer Vision and Large Language Models for Privacy-Preserve Pedestrian Activity Monitoring at Intersections—0
A lightweight YOLOv5-FFM model for occlusion pedestrian detection—0
Toward Pedestrian Head Tracking: A Benchmark Dataset and an Information Fusion Network—0
Nighttime Pedestrian Detection Based on Fore-Background Contrast Learning—0
The Research of Group Re-identification from Multiple Cameras—0
Evaluating and Enhancing Trustworthiness of LLMs in Perception Tasks—0
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All datasetsCaltechCityPersonsLLVIPDVTODTJU-Ped-trafficTJU-Ped-campusCVC14Caltech Pedestrian DatasetMMPD-Dataset

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1UniHCP (FT)Heavy MR^-227.2—Unverified
2LDCFReasonable Miss Rate24.8—Unverified
3AlexNetReasonable Miss Rate23.3—Unverified
4TA-CNNReasonable Miss Rate20.9—Unverified
5Checkerboards+Reasonable Miss Rate17.1—Unverified
6NNNFReasonable Miss Rate16.2—Unverified
7Part-level CNN + saliency and bounding box alignmentReasonable Miss Rate12.4—Unverified
8CompACT-DeepReasonable Miss Rate11.75—Unverified
9MCFReasonable Miss Rate10.4—Unverified
10MS-CNNReasonable Miss Rate9.95—Unverified
#ModelMetricClaimedVerifiedStatus
1ACSP + EuroCity PersonsHeavy MR^-242.5—Unverified
2TLLReasonable MR^-215.5—Unverified
3FRCNNReasonable MR^-215.4—Unverified
4FRCNN+SegReasonable MR^-214.8—Unverified
5TLL+MRFReasonable MR^-214.4—Unverified
6RepLossReasonable MR^-213.2—Unverified
7OR-CNNReasonable MR^-212.8—Unverified
8ALFNetReasonable MR^-212—Unverified
9CSP (with offset) + ResNet-50Reasonable MR^-211—Unverified
10NOH-NMSReasonable MR^-210.8—Unverified
#ModelMetricClaimedVerifiedStatus
1INSANetlog average miss rate4.43—Unverified
2MMPedestronAP0.73—Unverified
3CAFF-DINOAP0.69—Unverified
4CFTAP0.64—Unverified
5UniRGB-IRAP0.63—Unverified
6RSDetAP0.61—Unverified
7CMXAP0.6—Unverified
8CSSAAP0.59—Unverified
9GAFFAP0.56—Unverified
10Halfway FusionAP0.55—Unverified
#ModelMetricClaimedVerifiedStatus
1YOLOv6 (Thermal) mAP84.4—Unverified
2CFT mAP82.7—Unverified
3YOLOv3 (Thermal) mAP82.7—Unverified
4CMX mAP81.6—Unverified
5YOLOv7 (Thermal)mAP77.8—Unverified
6YOLOv6 (Visible) mAP38.1—Unverified
7YOLOv7 (Visible)mAP35.3—Unverified
8YOLOv3 (Visible) mAP34.5—Unverified
#ModelMetricClaimedVerifiedStatus
1FCOSR (miss rate)24.35—Unverified
2RetinaNetR (miss rate)23.89—Unverified
3FPNR (miss rate)22.3—Unverified
4CrowdDetR (miss rate)20.82—Unverified
5EGCLR (miss rate)19.73—Unverified
6LSFMR (miss rate)18.7—Unverified
#ModelMetricClaimedVerifiedStatus
1RetinaNetR (miss rate)34.73—Unverified
2FCOSR (miss rate)31.89—Unverified
3CrowdDetR (miss rate)25.73—Unverified
4EGCLR (miss rate)24.84—Unverified
#ModelMetricClaimedVerifiedStatus
1CFTAP5078.2—Unverified
2CMXAP5068.9—Unverified
#ModelMetricClaimedVerifiedStatus
1LSFMMR0.87—Unverified
#ModelMetricClaimedVerifiedStatus
1MMPedestronbox mAP79—Unverified