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 301–350 of 438 papers

TitleStatusHype
Multi-View Pedestrian Occupancy Prediction with a Novel Synthetic Dataset—0
Mutual-Supervised Feature Modulation Network for Occluded Pedestrian Detection—0
Near-chip Dynamic Vision Filtering for Low-Bandwidth Pedestrian Detection—0
Network Generalization Prediction for Safety Critical Tasks in Novel Operating Domains—0
NMS by Representative Region: Towards Crowded Pedestrian Detection by Proposal Pairing—0
Node-Adapt, Path-Adapt and Tree-Adapt:Model-Transfer Domain Adaptation for Random Forest—0
NU-AIR -- A Neuromorphic Urban Aerial Dataset for Detection and Localization of Pedestrians and Vehicles—0
Object as Hotspots: An Anchor-Free 3D Object Detection Approach via Firing of Hotspots—0
Object Detection Using Generalization and Efficiency Balanced Co-Occurrence Features—0
Object Detection with Deep Learning: A Review—0
Occluded Pedestrian Detection Through Guided Attention in CNNs—0
Occluded Pedestrian Detection with Visible IoU and Box Sign Predictor—0
Occlusion-aware R-CNN: Detecting Pedestrians in a Crowd—0
OmniDRL: Robust Pedestrian Detection using Deep Reinforcement Learning on Omnidirectional Cameras—0
Optimal Proposal Learning for Deployable End-to-End Pedestrian Detection—0
Optimized Pedestrian Detection for Multiple and Occluded People—0
Optimizing Camera Configurations for Multi-View Pedestrian Detection—0
Optimizing the image correction pipeline for pedestrian detection in the thermal-infrared domain—0
Panoramic Distortion-Aware Tokenization for Person Detection and Localization Using Transformers in Overhead Fisheye Images—0
ParGANDA: Making Synthetic Pedestrians A Reality For Object Detection—0
PathFinder: Attention-Driven Dynamic Non-Line-of-Sight Tracking with a Mobile Robot—0
PCN: Part and Context Information for Pedestrian Detection with CNNs—0
Pedestrain detection for low-light vision proposal—0
Pedestrian Detection aided by Deep Learning Semantic Tasks—0
Pedestrian Detection by Exemplar-Guided Contrastive Learning—0
Pedestrian Detection in 3D Point Clouds using Deep Neural Networks—0
Pedestrian Detection in Low-Light Conditions: A Comprehensive Survey—0
Pedestrian Detection in Low-resolution Imagery by Learning Multi-scale Intrinsic Motion Structures (MIMS)—0
Pedestrian Detection Inspired by Appearance Constancy and Shape Symmetry—0
Pedestrian detection with high-resolution event camera—0
Pedestrian Detection with Spatially Pooled Features and Structured Ensemble Learning—0
Pedestrian Detection with Unsupervised Multi-Stage Feature Learning—0
Pedestrian Detection with Wearable Cameras for the Blind: A Two-way Perspective—0
Pedestrian intention prediction in Adverse Weather Conditions with Spiking Neural Networks and Dynamic Vision Sensors—0
Pedestrian Tracking by Probabilistic Data Association and Correspondence Embeddings—0
PedHunter: Occlusion Robust Pedestrian Detector in Crowded Scenes—0
Person Re-identification in the Wild—0
Person Search via A Mask-Guided Two-Stream CNN Model—0
Physically-admissible polarimetric data augmentation for road-scene analysis—0
PMC-GANs: Generating Multi-Scale High-Quality Pedestrian with Multimodal Cascaded GANs—0
PPD: A New Valet Parking Pedestrian Fisheye Dataset for Autonomous Driving—0
Predicting the Influence of Adverse Weather on Pedestrian Detection with Automotive Radar and Lidar Sensors—0
Prediction of Occluded Pedestrians in Road Scenes using Human-like Reasoning: Insights from the OccluRoads Dataset—0
Progressive Refinement Network for Occluded Pedestrian Detection—0
PSC-Net: Learning Part Spatial Co-occurrence for Occluded Pedestrian Detection—0
PSDiff: Diffusion Model for Person Search with Iterative and Collaborative Refinement—0
Pushing the Limits of Deep CNNs for Pedestrian Detection—0
Quantum-soft QUBO Suppression for Accurate Object Detection—0
Ranking-based Adaptive Query Generation for DETRs in Crowded Pedestrian Detection—0
Real-time Aerial Detection and Reasoning on Embedded-UAVs—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