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
CARLA-BSP: a simulated dataset with pedestriansCode1
A Preliminary Study of Deep Learning Sensor Fusion for Pedestrian DetectionCode0
Adversarial Infrared Blocks: A Multi-view Black-box Attack to Thermal Infrared Detectors in Physical World—0
VLPD: Context-Aware Pedestrian Detection via Vision-Language Semantic Self-SupervisionCode1
Beyond Appearance: a Semantic Controllable Self-Supervised Learning Framework for Human-Centric Visual TasksCode3
Pedestrain detection for low-light vision proposal—0
HumanBench: Towards General Human-centric Perception with Projector Assisted PretrainingCode2
UniHCP: A Unified Model for Human-Centric PerceptionsCode1
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
MS-DETR: Multispectral Pedestrian Detection Transformer with Loosely Coupled Fusion and Modality-Balanced OptimizationCode1
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
Localized Semantic Feature Mixers for Efficient Pedestrian Detection in Autonomous Driving—0
Optimal Proposal Learning for Deployable End-to-End Pedestrian Detection—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
Comparison Of Deep Object Detectors On A New Vulnerable Pedestrian DatasetCode1
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
Untargeted Backdoor Attack against Object DetectionCode1
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
CrossDTR: Cross-view and Depth-guided Transformers for 3D Object DetectionCode1
Application of image-to-image translation in improving pedestrian detection—0
YOLOv6: A Single-Stage Object Detection Framework for Industrial ApplicationsCode5
Booster-SHOT: Boosting Stacked Homography Transformations for Multiview Pedestrian Detection with Attention—0
LMOT: Efficient Light-Weight Detection and Tracking in CrowdsCode1
Adaptive Feature Fusion for Cooperative Perception using LiDAR Point Clouds—0
Domain Adaptive Person SearchCode1
3D Random Occlusion and Multi-Layer Projection for Deep Multi-Camera Pedestrian LocalizationCode1
YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectorsCode7
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
PedRecNet: Multi-task deep neural network for full 3D human pose and orientation estimationCode1
Real-time HOG+SVM based object detection using SoC FPGA for a UHD video stream—0
STCrowd: A Multimodal Dataset for Pedestrian Perception in Crowded ScenesCode1
Confidence-aware Fusion using Dempster-Shafer Theory for Multispectral Pedestrian DetectionCode0
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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