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 351–400 of 438 papers

TitleStatusHype
Real-time HOG+SVM based object detection using SoC FPGA for a UHD video stream—0
Real-time Pedestrian Detection Approach with an Efficient Data Communication Bandwidth Strategy—0
Real-Time Pedestrian Detection on IoT Edge Devices: A Lightweight Deep Learning Approach—0
Real-Time RGB-D based Template Matching Pedestrian Detection—0
Reduced Memory Region Based Deep Convolutional Neural Network Detection—0
Research, Applications and Prospects of Event-Based Pedestrian Detection: A Survey—0
Resisting Crowd Occlusion and Hard Negatives for Pedestrian Detection in the Wild—0
Revisiting Misalignment in Multispectral Pedestrian Detection: A Language-Driven Approach for Cross-modal Alignment Fusion—0
Revisiting Modality Imbalance In Multimodal Pedestrian Detection—0
Road Detection via On--line Label Transfer—0
Robust Multi-resolution Pedestrian Detection in Traffic Scenes—0
Robust Multi-view Pedestrian Tracking Using Neural Networks—0
Robust pedestrian detection in thermal imagery using synthesized images—0
Robust Pedestrian Detection via Constructing Versatile Pedestrian Knowledge Bank—0
Robust Real-time Pedestrian Detection in Aerial Imagery on Jetson TX2—0
Robust Real-Time Pedestrian Detection on Embedded Devices—0
Robust Small-Scale Pedestrian Detection With Cued Recall via Memory Learning—0
Rotational Rectification Network: Enabling Pedestrian Detection for Mobile Vision—0
SADet: Learning An Efficient and Accurate Pedestrian Detector—0
SAM-RCNN: Scale-Aware Multi-Resolution Multi-Channel Pedestrian Detection—0
Scale-aware Fast R-CNN for Pedestrian Detection—0
SDR-GAIN: A High Real-Time Occluded Pedestrian Pose Completion Method for Autonomous Driving—0
Seek and You Will Find: A New Optimized Framework for Efficient Detection of Pedestrian—0
Segmentation-Based Bounding Box Generation for Omnidirectional Pedestrian Detection—0
Selecting Learnable Training Samples is All DETRs Need in Crowded Pedestrian Detection—0
Self-learning Scene-specific Pedestrian Detectors using a Progressive Latent Model—0
Self-Supervised Learning for Point Clouds Data: A Survey—0
Semantic Channels for Fast Pedestrian Detection—0
Semantic Driven Multi-Camera Pedestrian Detection—0
Semantic Head Enhanced Pedestrian Detection in a Crowd—0
SemanticVoxels: Sequential Fusion for 3D Pedestrian Detection using LiDAR Point Cloud and Semantic Segmentation—0
Semi-Supervised Pedestrian Instance Synthesis and Detection With Mutual Reinforcement—0
Simultaneous Object Detection and Ranking with Weak Supervision—0
Single-Pedestrian Detection Aided by Multi-pedestrian Detection—0
Single Shot Multitask Pedestrian Detection and Behavior Prediction—0
Linear Support Tensor Machine: Pedestrian Detection in Thermal Infrared ImagesCode0
Learning to Separate: Detecting Heavily-Occluded Objects in Urban ScenesCode0
Segmentation Mask Guided End-to-End Person SearchCode0
Bias Behind the Wheel: Fairness Testing of Autonomous Driving SystemsCode0
Active Learning for Deep Detection Neural NetworksCode0
Count- and Similarity-aware R-CNN for Pedestrian DetectionCode0
A Preliminary Study of Deep Learning Sensor Fusion for Pedestrian DetectionCode0
Mask-Guided Attention Network for Occluded Pedestrian DetectionCode0
Part-Level Convolutional Neural Networks for Pedestrian Detection Using Saliency and Boundary Box AlignmentCode0
Why do linear SVMs trained on HOG features perform so well?Code0
Deep Intra-Image Contrastive Learning for Weakly Supervised One-Step Person SearchCode0
Benchmarking person re-identification datasets and approaches for practical real-world implementationsCode0
Learning Scene-Pedestrian Graph for End to end Person SearchCode0
PedDet: Adaptive Spectral Optimization for Multimodal Pedestrian DetectionCode0
Learning Efficient Single-stage Pedestrian Detectors by Asymptotic Localization FittingCode0
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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