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 351–400 of 438 papers

TitleStatusHype
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
Flexible Network Binarization with Layer-wise Priority—0
Too Far to See? Not Really! --- Pedestrian Detection with Scale-aware Localization Policy—0
Gradient-based Camera Exposure Control for Outdoor Mobile Platforms—0
MixedPeds: Pedestrian Detection in Unannotated Videos using Synthetically Generated Human-agents for Training—0
The WILDTRACK Multi-Camera Person Dataset—0
Comparing Apples and Oranges: Off-Road Pedestrian Detection on the NREC Agricultural Person-Detection Dataset—0
Image Segmentation Algorithms Overview—0
Illuminating Pedestrians via Simultaneous Detection & SegmentationCode0
Rotational Rectification Network: Enabling Pedestrian Detection for Mobile Vision—0
What Can Help Pedestrian Detection?—0
Robust Multi-view Pedestrian Tracking Using Neural Networks—0
Accurate Single Stage Detector Using Recurrent Rolling ConvolutionCode0
Learning Cross-Modal Deep Representations for Robust Pedestrian DetectionCode0
Expecting the Unexpected: Training Detectors for Unusual Pedestrians with Adversarial ImpostersCode0
To Boost or Not to Boost? On the Limits of Boosted Trees for Object Detection—0
In Teacher We Trust: Learning Compressed Models for Pedestrian Detection—0
DeepSetNet: Predicting Sets with Deep Neural Networks—0
Self-learning Scene-specific Pedestrian Detectors using a Progressive Latent Model—0
Node-Adapt, Path-Adapt and Tree-Adapt:Model-Transfer Domain Adaptation for Random Forest—0
GPU-based Pedestrian Detection for Autonomous Driving—0
Fused DNN: A deep neural network fusion approach to fast and robust pedestrian detection—0
Real-Time RGB-D based Template Matching Pedestrian Detection—0
Linear Support Tensor Machine: Pedestrian Detection in Thermal Infrared ImagesCode0
Reduced Memory Region Based Deep Convolutional Neural Network Detection—0
A Unified Multi-scale Deep Convolutional Neural Network for Fast Object DetectionCode0
Is Faster R-CNN Doing Well for Pedestrian Detection?—0
A Real-Time Deep Learning Pedestrian Detector for Robot Navigation—0
Efficient and Robust Pedestrian Detection using Deep Learning for Human-Aware Navigation—0
LCrowdV: Generating Labeled Videos for Simulation-based Crowd Behavior Learning—0
Large Scale Hard Sample Mining With Monte Carlo Tree Search—0
Semantic Channels for Fast Pedestrian Detection—0
Adaptive Algorithm and Platform Selection for Visual Detection and Tracking—0
Person Re-identification in the Wild—0
Joint Detection and Identification Feature Learning for Person SearchCode0
Pushing the Limits of Deep CNNs for Pedestrian Detection—0
Learning Multilayer Channel Features for Pedestrian Detection—0
How Far are We from Solving Pedestrian Detection?—0
Deep Learning Strong Parts for Pedestrian Detection—0
Object Detection Using Generalization and Efficiency Balanced Co-Occurrence Features—0
Pedestrian Detection Inspired by Appearance Constancy and Shape Symmetry—0
A convnet for non-maximum suppression—0
Scale-aware Fast R-CNN for Pedestrian Detection—0
Deep convolutional neural networks for pedestrian detectionCode0
Learning Complexity-Aware Cascades for Deep Pedestrian Detection—0
Multispectral Pedestrian Detection: Benchmark Dataset and Baseline—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
2YOLOv3 (Thermal) mAP82.7—Unverified
3CFT 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