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 201–250 of 438 papers

TitleStatusHype
RGB-D Railway Platform Monitoring and Scene Understanding for Enhanced Passenger SafetyCode1
Decoupled and Memory-Reinforced Networks: Towards Effective Feature Learning for One-Step Person SearchCode0
Robust pedestrian detection in thermal imagery using synthesized images—0
Box Re-Ranking: Unsupervised False Positive Suppression for Domain Adaptive Pedestrian Detection—0
LLA: Loss-aware Label Assignment for Dense Pedestrian DetectionCode1
Single Shot Multitask Pedestrian Detection and Behavior Prediction—0
Guided Attentive Feature Fusion for Multispectral Pedestrian DetectionCode1
Robust Small-Scale Pedestrian Detection With Cued Recall via Memory Learning—0
Stacked Homography Transformations for Multi-View Pedestrian Detection—0
Body-Face Joint Detection via Embedding and Head HookCode1
Infrared image pedestrian target detection based on Yolov3 and migration learning—0
Where, What, Whether: Multi-modal Learning Meets Pedestrian Detection—0
Robust Real-Time Pedestrian Detection on Embedded Devices—0
DETR for Crowd Pedestrian DetectionCode1
Impact of Power Supply Noise on Image Sensor Performance in Automotive Applications—0
Characterisation of CMOS Image Sensor Performance in Low Light Automotive Applications—0
TJU-DHD: A Diverse High-Resolution Dataset for Object DetectionCode1
Mutual-Supervised Feature Modulation Network for Occluded Pedestrian Detection—0
Analysis of pedestrian activity before and during COVID-19 lockdown, using webcam time-lapse from Cracow and machine learningCode0
From Handcrafted to Deep Features for Pedestrian Detection: A SurveyCode1
SemanticVoxels: Sequential Fusion for 3D Pedestrian Detection using LiDAR Point Cloud and Semantic Segmentation—0
Efficient Pedestrian Detection in Top-View Fisheye Images Using Compositions of Perspective View Patches—0
Visible Feature Guidance for Crowd Pedestrian Detection—0
Anchor-free Small-scale Multispectral Pedestrian DetectionCode1
Improving Multispectral Pedestrian Detection by Addressing Modality Imbalance ProblemsCode1
Progressive Refinement Network for Occluded Pedestrian Detection—0
Task-conditioned Domain Adaptation for Pedestrian Detection in Thermal ImageryCode1
Count- and Similarity-aware R-CNN for Pedestrian DetectionCode0
Quantum-soft QUBO Suppression for Accurate Object Detection—0
NOH-NMS: Improving Pedestrian Detection by Nearby Objects HallucinationCode1
YOLOpeds: Efficient Real-Time Single-Shot Pedestrian Detection for Smart Camera Applications—0
SADet: Learning An Efficient and Accurate Pedestrian Detector—0
Multiview Detection with Feature Perspective TransformationCode1
LRPD: Long Range 3D Pedestrian Detection Leveraging Specific Strengths of LiDAR and RGB—0
Temporal-Context Enhanced Detection of Heavily Occluded Pedestrians—0
Resisting Crowd Occlusion and Hard Negatives for Pedestrian Detection in the Wild—0
STINet: Spatio-Temporal-Interactive Network for Pedestrian Detection and Trajectory Prediction—0
Occluded Prohibited Items Detection: an X-ray Security Inspection Benchmark and De-occlusion Attention ModuleCode1
Gradient-based Feature Extraction From Raw Bayer Pattern Images—0
The P-DESTRE: A Fully Annotated Dataset for Pedestrian Detection, Tracking, Re-Identification and Search from Aerial Devices—0
Near-chip Dynamic Vision Filtering for Low-Bandwidth Pedestrian Detection—0
When Autonomous Systems Meet Accuracy and Transferability through AI: A Survey—0
NMS by Representative Region: Towards Crowded Pedestrian Detection by Proposal Pairing—0
Pedestrian Detection with Wearable Cameras for the Blind: A Two-way Perspective—0
Detection in Crowded Scenes: One Proposal, Multiple PredictionsCode2
Generalizable Pedestrian Detection: The Elephant In The Room—0
Adapted Center and Scale Prediction: More Stable and More AccurateCode1
An End-to-End Framework for Unsupervised Pose Estimation of Occluded Pedestrians—0
Input Dropout for Spatially Aligned ModalitiesCode0
Measuring the Utilization of Public Open Spaces by Deep Learning: a Benchmark Study at the Detroit Riverfront—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