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 301–350 of 438 papers

TitleStatusHype
WIDER Face and Pedestrian Challenge 2018: Methods and Results—0
Fast Pedestrian Detection based on T-CENTRIST in infrared image—0
Box-level Segmentation Supervised Deep Neural Networks for Accurate and Real-time Multispectral Pedestrian Detection—0
Towards Pedestrian Detection Using RetinaNet in ECCV 2018 Wider Pedestrian Detection ChallengeCode0
Semantic Driven Multi-Camera Pedestrian Detection—0
Beyond Domain Adaptation: Unseen Domain Encapsulation via Universal Non-volume Preserving Models—0
Pedestrian Detection with Autoregressive Network PhasesCode0
Deep Learning based Pedestrian Detection at Distance in Smart Cities—0
Part-Level Convolutional Neural Networks for Pedestrian Detection Using Saliency and Boundary Box AlignmentCode0
CADP: A Novel Dataset for CCTV Traffic Camera based Accident AnalysisCode0
An FPGA-Accelerated Design for Deep Learning Pedestrian Detection in Self-Driving Vehicles—0
Comparing Computing Platforms for Deep Learning on a Humanoid Robot—0
Constraint-Aware Deep Neural Network Compression—0
Learning Efficient Single-stage Pedestrian Detectors by Asymptotic Localization FittingCode0
Small-scale Pedestrian Detection Based on Topological Line Localization and Temporal Feature Aggregation—0
Bi-box Regression for Pedestrian Detection and Occlusion Estimation—0
Graininess-Aware Deep Feature Learning for Pedestrian Detection—0
Real-time Pedestrian Detection Approach with an Efficient Data Communication Bandwidth Strategy—0
Multispectral Pedestrian Detection via Simultaneous Detection and SegmentationCode0
Unsupervised Hard Example Mining from Videos for Improved Object Detection—0
SAM-RCNN: Scale-Aware Multi-Resolution Multi-Channel Pedestrian Detection—0
Occlusion-aware R-CNN: Detecting Pedestrians in a Crowd—0
Person Search via A Mask-Guided Two-Stream CNN Model—0
Convolutional Neural Networks for Aerial Multi-Label Pedestrian Detection—0
Is the Pedestrian going to Cross? Answering by 2D Pose Estimation—0
Object Detection with Deep Learning: A Review—0
Robustness Analysis of Pedestrian Detectors for SurveillanceCode0
Small-scale Pedestrian Detection Based on Somatic Topology Localization and Temporal Feature Aggregation—0
A Content-Based Late Fusion Approach Applied to Pedestrian Detection—0
WILDTRACK: A Multi-Camera HD Dataset for Dense Unscripted Pedestrian Detection—0
Improving Occlusion and Hard Negative Handling for Single-Stage Pedestrian Detectors—0
Exploit the Unknown Gradually: One-Shot Video-Based Person Re-Identification by Stepwise Learning—0
Occluded Pedestrian Detection Through Guided Attention in CNNs—0
Disparity Sliding Window: Object Proposals From Disparity ImagesCode0
Fused Deep Neural Networks for Efficient Pedestrian Detection—0
CrowdHuman: A Benchmark for Detecting Human in a CrowdCode1
PCN: Part and Context Information for Pedestrian Detection with CNNs—0
YOLOv3: An Incremental ImprovementCode1
Pedestrian-Synthesis-GAN: Generating Pedestrian Data in Real Scene and BeyondCode0
Exploring Multi-Branch and High-Level Semantic Networks for Improving Pedestrian Detection—0
End-to-End Detection and Re-identification Integrated Net for Person Search—0
Illumination-aware Faster R-CNN for Robust Multispectral Pedestrian Detection—0
Fusion of Multispectral Data Through Illumination-aware Deep Neural Networks for Pedestrian Detection—0
Unsupervised Deep Domain Adaptation for Pedestrian Detection—0
A High-Performance HOG Extractor on FPGA—0
Aggregated Channels Network for Real-Time Pedestrian Detection—0
Scene-Specific Pedestrian Detection Based on Parallel VisionCode0
Repulsion Loss: Detecting Pedestrians in a CrowdCode0
Combining LiDAR Space Clustering and Convolutional Neural Networks for Pedestrian Detection—0
Multi-Label Learning of Part Detectors for Heavily Occluded 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