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 251–300 of 438 papers

TitleStatusHype
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
Small-scale Pedestrian Detection Based on Somatic Topology Localization and Temporal Feature Aggregation—0
Small-scale Pedestrian Detection Based on Topological Line Localization and Temporal Feature Aggregation—0
Smart IoT Cameras for Crowd Analysis based on augmentation for automatic pedestrian detection, simulation and annotation—0
VeriCompress: A Tool to Streamline the Synthesis of Verified Robust Compressed Neural Networks from Scratch—0
Spatio-Contextual Deep Network Based Multimodal Pedestrian Detection For Autonomous Driving—0
Spatiotemporal Stacked Sequential Learning for Pedestrian Detection—0
SSA-CNN: Semantic Self-Attention CNN for Pedestrian Detection—0
Stabilizing multispectral pedestrian detection with evidential hybrid fusion—0
Stable and Effective One-Step Method for Person Search—0
Stacked Homography Transformations for Multi-View Pedestrian Detection—0
StarNet: Targeted Computation for Object Detection in Point Clouds—0
STINet: Spatio-Temporal-Interactive Network for Pedestrian Detection and Trajectory Prediction—0
Strengthening the Effectiveness of Pedestrian Detection with Spatially Pooled Features—0
Switchable Deep Network for Pedestrian Detection—0
Taking a Deeper Look at Pedestrians—0
Temporal-Context Enhanced Detection of Heavily Occluded Pedestrians—0
Ten Years of Pedestrian Detection, What Have We Learned?—0
The Pedestrian Patterns Dataset—0
The Research of Group Re-identification from Multiple Cameras—0
The WILDTRACK Multi-Camera Person Dataset—0
To Boost or Not to Boost? On the Limits of Boosted Trees for Object Detection—0
Too Far to See? Not Really! --- Pedestrian Detection with Scale-aware Localization Policy—0
To the Point: Efficient 3D Object Detection in the Range Image with Graph Convolution Kernels—0
Toward Pedestrian Head Tracking: A Benchmark Dataset and an Information Fusion Network—0
Toward unlabeled multi-view 3D pedestrian detection by generalizable AI: techniques and performance analysis—0
Traffic-Net: 3D Traffic Monitoring Using a Single Camera—0
t-RAIN: Robust generalization under weather-aliasing label shift attacks—0
Translation, Scale and Rotation: Cross-Modal Alignment Meets RGB-Infrared Vehicle Detection—0
Understanding the Impact of Edge Cases from Occluded Pedestrians for ML Systems—0
Unsupervised Deep Domain Adaptation for Pedestrian Detection—0
Unsupervised Domain Adaptation for Multispectral Pedestrian Detection—0
Unsupervised Hard Example Mining from Videos for Improved Object Detection—0
Unsupervised Multi-view Pedestrian Detection—0
V2F-Net: Explicit Decomposition of Occluded Pedestrian Detection—0
VALERIE22 -- A photorealistic, richly metadata annotated dataset of urban environments—0
Variational Pedestrian Detection—0
Verification of Size Invariance in DNN Activations using Concept Embeddings—0
Video-to-Text Pedestrian Monitoring (VTPM): Leveraging Computer Vision and Large Language Models for Privacy-Preserve Pedestrian Activity Monitoring at Intersections—0
Virtual to Real adaptation of Pedestrian Detectors—0
Visible Feature Guidance for Crowd Pedestrian Detection—0
Visible to Thermal image Translation for improving visual task in low light conditions—0
WCCNet: Wavelet-integrated CNN with Crossmodal Rearranging Fusion for Fast Multispectral Pedestrian Detection—0
What Can Help Pedestrian Detection?—0
When Autonomous Systems Meet Accuracy and Transferability through AI: A Survey—0
Where, What, Whether: Multi-modal Learning Meets Pedestrian Detection—0
Which to Match? Selecting Consistent GT-Proposal Assignment for Pedestrian Detection—0
WIDER Face and Pedestrian Challenge 2018: Methods and Results—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