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 101–150 of 438 papers

TitleStatusHype
Data-Driven but Privacy-Conscious: Pedestrian Dataset De-identification via Full-Body Person Synthesis—0
GMC: A General Framework of Multi-stage Context Learning and Utilization for Visual Detection Tasks—0
Data Augmentation in Human-Centric Vision—0
Beta R-CNN: Looking into Pedestrian Detection from Another Perspective—0
Basis Mapping Based Boosting for Object Detection—0
Deep Learning Strong Parts for Pedestrian Detection—0
Generation of Synthetic Images for Pedestrian Detection Using a Sequence of GANs—0
Deep Multi-Task Networks For Occluded Pedestrian Pose Estimation—0
DeepSetNet: Predicting Sets with Deep Neural Networks—0
BAANet: Learning Bi-directional Adaptive Attention Gates for Multispectral Pedestrian Detection—0
Adaptive Algorithm and Platform Selection for Visual Detection and Tracking—0
GFD-SSD: Gated Fusion Double SSD for Multispectral Pedestrian Detection—0
GPU-based Pedestrian Detection for Autonomous Driving—0
DINF: Dynamic Instance Noise Filter for Occluded Pedestrian Detection—0
Graininess-Aware Deep Feature Learning for Pedestrian Detection—0
Discriminative Feature Transformation for Occluded Pedestrian Detection—0
Distance Estimation in Outdoor Driving Environments Using Phase-only Correlation Method with Event Cameras—0
Distant Pedestrian Detection in the Wild using Single Shot Detector with Deep Convolutional Generative Adversarial Networks—0
DMRNet++: Learning Discriminative Features with Decoupled Networks and Enriched Pairs for One-Step Person Search—0
Cross-Modality Proposal-guided Feature Mining for Unregistered RGB-Thermal Pedestrian Detection—0
Automatic Dataset Augmentation Using Virtual Human Simulation—0
EdgeNet: Balancing Accuracy and Performance for Edge-based Convolutional Neural Network Object Detectors—0
Cross-Modal Analysis of Human Detection for Robotics: An Industrial Case Study—0
Efficient and Robust Pedestrian Detection using Deep Learning for Human-Aware Navigation—0
Efficient Learning of Pinball TWSVM using Privileged Information and its applications—0
Efficient pedestrian detection by directly optimize the partial area under the ROC curve—0
Nighttime Pedestrian Detection Based on Fore-Background Contrast Learning—0
Coupled Network for Robust Pedestrian Detection with Gated Multi-Layer Feature Extraction and Deformable Occlusion Handling—0
Attention-Aware Multi-View Pedestrian Tracking—0
Fused Deep Neural Networks for Efficient Pedestrian Detection—0
Convolutional Neural Networks for Aerial Multi-Label Pedestrian Detection—0
A Method for Crash Prediction and Avoidance Using Hidden Markov Models—0
Fused DNN: A deep neural network fusion approach to fast and robust pedestrian detection—0
Fusion of Multispectral Data Through Illumination-aware Deep Neural Networks for Pedestrian Detection—0
A Survey of Vision Transformers in Autonomous Driving: Current Trends and Future Directions—0
Context-Sensitive Decision Forests for Object Detection—0
A lightweight YOLOv5-FFM model for occlusion pedestrian detection—0
Constraint-Aware Deep Neural Network Compression—0
Confidence-Rated Multiple Instance Boosting for Object Detection—0
A Study on Mitigating Hard Boundaries of Decision-Tree-based Uncertainty Estimates for AI Models—0
ASPED: An Audio Dataset for Detecting Pedestrians—0
Computer vision in automated parking systems: Design, implementation and challenges—0
A Shape Transformation-based Dataset Augmentation Framework for Pedestrian Detection—0
Comparing Computing Platforms for Deep Learning on a Humanoid Robot—0
A High-Performance HOG Extractor on FPGA—0
From Pedestrian Detection to Crosswalk Estimation: An EM Algorithm and Analysis on Diverse Datasets—0
Gated Multi-layer Convolutional Feature Extraction Network for Robust Pedestrian Detection—0
Comparing Apples and Oranges: Off-Road Pedestrian Detection on the NREC Agricultural Person-Detection Dataset—0
Comparative study of multi-person tracking methods—0
A Safety-Adapted Loss for Pedestrian Detection in Automated Driving—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