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 151–200 of 438 papers

TitleStatusHype
Unsupervised Multi-view Pedestrian Detection—0
Real-time Aerial Detection and Reasoning on Embedded-UAVs—0
Selecting Learnable Training Samples is All DETRs Need in Crowded Pedestrian Detection—0
Diffusion Dataset Generation: Towards Closing the Sim2Real Gap for Pedestrian Detection—0
t-RAIN: Robust generalization under weather-aliasing label shift attacks—0
Self-Supervised Learning for Point Clouds Data: A Survey—0
Pedestrian Behavior Maps for Safety Advisories: CHAMP Framework and Real-World Data AnalysisCode0
6G Integrated Sensing and Communication: From Vision to Realization—0
Adversarial Infrared Blocks: A Multi-view Black-box Attack to Thermal Infrared Detectors in Physical World—0
A Preliminary Study of Deep Learning Sensor Fusion for Pedestrian DetectionCode0
Pedestrain detection for low-light vision proposal—0
Mesh-SORT: Simple and effective location-wise tracker with lost management strategies—0
Revisiting Modality Imbalance In Multimodal Pedestrian Detection—0
NU-AIR -- A Neuromorphic Urban Aerial Dataset for Detection and Localization of Pedestrians and Vehicles—0
Cascaded information enhancement and cross-modal attention feature fusion for multispectral pedestrian detection—0
Deep Intra-Image Contrastive Learning for Weakly Supervised One-Step Person SearchCode0
Real-Time High-Resolution Pedestrian Detection in Crowded Scenes via Parallel Edge Offloading—0
CHAMP: Crowdsourced, History-Based Advisory of Mapped Pedestrians for Safer Driver Assistance Systems—0
DINF: Dynamic Instance Noise Filter for Occluded Pedestrian Detection—0
Optimal Proposal Learning for Deployable End-to-End Pedestrian Detection—0
Localized Semantic Feature Mixers for Efficient Pedestrian Detection in Autonomous Driving—0
A Method for Crash Prediction and Avoidance Using Hidden Markov Models—0
Benchmarking person re-identification datasets and approaches for practical real-world implementationsCode0
CircleNet: Reciprocating Feature Adaptation for Robust Pedestrian Detection—0
Feature Calibration Network for Occluded Pedestrian Detection—0
Increasing pedestrian detection performance through weighting of detection impairing factors—0
VeriCompress: A Tool to Streamline the Synthesis of Verified Robust Compressed Neural Networks from Scratch—0
DMRNet++: Learning Discriminative Features with Decoupled Networks and Enriched Pairs for One-Step Person Search—0
Beta R-CNN: Looking into Pedestrian Detection from Another Perspective—0
A Robust Pedestrian Detection Approach for Autonomous Vehicles—0
Translation, Scale and Rotation: Cross-Modal Alignment Meets RGB-Infrared Vehicle Detection—0
Application of image-to-image translation in improving pedestrian detection—0
Booster-SHOT: Boosting Stacked Homography Transformations for Multiview Pedestrian Detection with Attention—0
Adaptive Feature Fusion for Cooperative Perception using LiDAR Point Clouds—0
Deep Multi-Task Networks For Occluded Pedestrian Pose Estimation—0
Physically-admissible polarimetric data augmentation for road-scene analysis—0
From Pedestrian Detection to Crosswalk Estimation: An EM Algorithm and Analysis on Diverse Datasets—0
An Objective Method for Pedestrian Occlusion Level Classification—0
The Impact of Partial Occlusion on Pedestrian DetectabilityCode0
Understanding the Impact of Edge Cases from Occluded Pedestrians for ML Systems—0
Real-time HOG+SVM based object detection using SoC FPGA for a UHD video stream—0
Confidence-aware Fusion using Dempster-Shafer Theory for Multispectral Pedestrian DetectionCode0
DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object DetectionCode0
A-Eye: Driving with the Eyes of AI for Corner Case Generation—0
End-to-end Person Search Sequentially Trained on Aggregated Dataset—0
A Study on Mitigating Hard Boundaries of Decision-Tree-based Uncertainty Estimates for AI Models—0
Birds Eye View Social Distancing Analysis System—0
Embracing Single Stride 3D Object Detector with Sparse TransformerCode0
Illumination and Temperature-Aware Multispectral Networks for Edge-Computing-Enabled Pedestrian Detection—0
BAANet: Learning Bi-directional Adaptive Attention Gates for Multispectral Pedestrian 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