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
Combining LiDAR Space Clustering and Convolutional Neural Networks for Pedestrian Detection—0
Comparative study of multi-person tracking methods—0
Comparing Apples and Oranges: Off-Road Pedestrian Detection on the NREC Agricultural Person-Detection Dataset—0
Comparing Computing Platforms for Deep Learning on a Humanoid Robot—0
Computer vision in automated parking systems: Design, implementation and challenges—0
Confidence-Rated Multiple Instance Boosting for Object Detection—0
Constraint-Aware Deep Neural Network Compression—0
Context-Sensitive Decision Forests for Object Detection—0
Convolutional Neural Networks for Aerial Multi-Label Pedestrian Detection—0
Coupled Network for Robust Pedestrian Detection with Gated Multi-Layer Feature Extraction and Deformable Occlusion Handling—0
Cross-Modal Analysis of Human Detection for Robotics: An Industrial Case Study—0
Cross-Modality Proposal-guided Feature Mining for Unregistered RGB-Thermal Pedestrian Detection—0
Data Augmentation in Human-Centric Vision—0
Data-Driven but Privacy-Conscious: Pedestrian Dataset De-identification via Full-Body Person Synthesis—0
Deep Learning based Pedestrian Detection at Distance in Smart Cities—0
Deep Learning Strong Parts for Pedestrian Detection—0
Deep Multi-Task Networks For Occluded Pedestrian Pose Estimation—0
DeepSetNet: Predicting Sets with Deep Neural Networks—0
Detector With Focus: Normalizing Gradient In Image Pyramid—0
Diffusion Dataset Generation: Towards Closing the Sim2Real Gap for Pedestrian Detection—0
DINF: Dynamic Instance Noise Filter for Occluded 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
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
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
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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