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 401–438 of 438 papers

TitleStatusHype
Exploit the Unknown Gradually: One-Shot Video-Based Person Re-Identification by Stepwise Learning—0
Exploring Human Vision Driven Features for Pedestrian Detection—0
Exploring Multi-Branch and High-Level Semantic Networks for Improving Pedestrian Detection—0
Exploring Weak Stabilization for Motion Feature Extraction—0
Fast keypoint detection in video sequences—0
Fast Multiple-Part Based Object Detection Using KD-Ferns—0
Fast Pedestrian Detection based on T-CENTRIST in infrared image—0
Feature Calibration Network for Occluded Pedestrian Detection—0
Filtered Channel Features for Pedestrian Detection—0
Filtered Feature Channels for Pedestrian Detection—0
FisheyeDetNet: 360° Surround view Fisheye Camera based Object Detection System for Autonomous Driving—0
Flexible Network Binarization with Layer-wise Priority—0
Adversarial Infrared Blocks: A Multi-view Black-box Attack to Thermal Infrared Detectors in Physical World—0
From Pedestrian Detection to Crosswalk Estimation: An EM Algorithm and Analysis on Diverse Datasets—0
Fused Deep Neural Networks for Efficient Pedestrian Detection—0
Fused DNN: A deep neural network fusion approach to fast and robust pedestrian detection—0
Fusion of Multispectral Data Through Illumination-aware Deep Neural Networks for Pedestrian Detection—0
Gated Multi-layer Convolutional Feature Extraction Network for Robust Pedestrian Detection—0
Generalizable Multi-Camera 3D Pedestrian Detection—0
Generation of Synthetic Images for Pedestrian Detection Using a Sequence of GANs—0
GFD-SSD: Gated Fusion Double SSD for Multispectral Pedestrian Detection—0
Gliding vertex on the horizontal bounding box for multi-oriented object detection—0
Global-Local Dynamic Feature Alignment Network for Person Re-Identification—0
GMC: A General Framework of Multi-stage Context Learning and Utilization for Visual Detection Tasks—0
GPU-based Pedestrian Detection for Autonomous Driving—0
Gradient-based Camera Exposure Control for Outdoor Mobile Platforms—0
Gradient-based Feature Extraction From Raw Bayer Pattern Images—0
Graininess-Aware Deep Feature Learning for Pedestrian Detection—0
GSON: A Group-based Social Navigation Framework with Large Multimodal Model—0
Hierarchical Adaptive Structural SVM for Domain Adaptation—0
How Far are We from Solving Pedestrian Detection?—0
Human Extraction and Scene Transition utilizing Mask R-CNN—0
Hybrid Attention for Robust RGB-T Pedestrian Detection in Real-World Conditions—0
Hybrid Channel Based Pedestrian Detection—0
Illumination and Temperature-Aware Multispectral Networks for Edge-Computing-Enabled Pedestrian Detection—0
Illumination-aware Faster R-CNN for Robust Multispectral Pedestrian Detection—0
Image Segmentation Algorithms Overview—0
Imagine the Unseen: Occluded Pedestrian Detection via Adversarial Feature Completion—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