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

Showing 101125 of 438 papers

TitleStatusHype
Exploring Weak Stabilization for Motion Feature Extraction0
Data Augmentation in Human-Centric Vision0
Basis Mapping Based Boosting for Object Detection0
Exploring Human Vision Driven Features for Pedestrian Detection0
BAANet: Learning Bi-directional Adaptive Attention Gates for Multispectral Pedestrian Detection0
Deep Learning Strong Parts for Pedestrian Detection0
Adaptive Algorithm and Platform Selection for Visual Detection and Tracking0
Deep Multi-Task Networks For Occluded Pedestrian Pose Estimation0
DeepSetNet: Predicting Sets with Deep Neural Networks0
Exploring Multi-Branch and High-Level Semantic Networks for Improving Pedestrian Detection0
Fast Multiple-Part Based Object Detection Using KD-Ferns0
Feature Calibration Network for Occluded Pedestrian Detection0
Cross-Modality Proposal-guided Feature Mining for Unregistered RGB-Thermal Pedestrian Detection0
DINF: Dynamic Instance Noise Filter for Occluded Pedestrian Detection0
Automatic Dataset Augmentation Using Virtual Human Simulation0
Discriminative Feature Transformation for Occluded Pedestrian Detection0
Distance Estimation in Outdoor Driving Environments Using Phase-only Correlation Method with Event Cameras0
Distant Pedestrian Detection in the Wild using Single Shot Detector with Deep Convolutional Generative Adversarial Networks0
DMRNet++: Learning Discriminative Features with Decoupled Networks and Enriched Pairs for One-Step Person Search0
Cross-Modal Analysis of Human Detection for Robotics: An Industrial Case Study0
Nighttime Pedestrian Detection Based on Fore-Background Contrast Learning0
EdgeNet: Balancing Accuracy and Performance for Edge-based Convolutional Neural Network Object Detectors0
Coupled Network for Robust Pedestrian Detection with Gated Multi-Layer Feature Extraction and Deformable Occlusion Handling0
Efficient and Robust Pedestrian Detection using Deep Learning for Human-Aware Navigation0
Attention-Aware Multi-View Pedestrian Tracking0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1UniHCP (FT)Heavy MR^-227.2Unverified
2LDCFReasonable Miss Rate24.8Unverified
3AlexNetReasonable Miss Rate23.3Unverified
4TA-CNNReasonable Miss Rate20.9Unverified
5Checkerboards+Reasonable Miss Rate17.1Unverified
6NNNFReasonable Miss Rate16.2Unverified
7Part-level CNN + saliency and bounding box alignmentReasonable Miss Rate12.4Unverified
8CompACT-DeepReasonable Miss Rate11.75Unverified
9MCFReasonable Miss Rate10.4Unverified
10MS-CNNReasonable Miss Rate9.95Unverified
#ModelMetricClaimedVerifiedStatus
1ACSP + EuroCity PersonsHeavy MR^-242.5Unverified
2TLLReasonable MR^-215.5Unverified
3FRCNNReasonable MR^-215.4Unverified
4FRCNN+SegReasonable MR^-214.8Unverified
5TLL+MRFReasonable MR^-214.4Unverified
6RepLossReasonable MR^-213.2Unverified
7OR-CNNReasonable MR^-212.8Unverified
8ALFNetReasonable MR^-212Unverified
9CSP (with offset) + ResNet-50Reasonable MR^-211Unverified
10NOH-NMSReasonable MR^-210.8Unverified
#ModelMetricClaimedVerifiedStatus
1INSANetlog average miss rate4.43Unverified
2MMPedestronAP0.73Unverified
3CAFF-DINOAP0.69Unverified
4CFTAP0.64Unverified
5UniRGB-IRAP0.63Unverified
6RSDetAP0.61Unverified
7CMXAP0.6Unverified
8CSSAAP0.59Unverified
9GAFFAP0.56Unverified
10Halfway FusionAP0.55Unverified
#ModelMetricClaimedVerifiedStatus
1YOLOv6 (Thermal) mAP84.4Unverified
2CFT mAP82.7Unverified
3YOLOv3 (Thermal) mAP82.7Unverified
4CMX mAP81.6Unverified
5YOLOv7 (Thermal)mAP77.8Unverified
6YOLOv6 (Visible) mAP38.1Unverified
7YOLOv7 (Visible)mAP35.3Unverified
8YOLOv3 (Visible) mAP34.5Unverified
#ModelMetricClaimedVerifiedStatus
1FCOSR (miss rate)24.35Unverified
2RetinaNetR (miss rate)23.89Unverified
3FPNR (miss rate)22.3Unverified
4CrowdDetR (miss rate)20.82Unverified
5EGCLR (miss rate)19.73Unverified
6LSFMR (miss rate)18.7Unverified
#ModelMetricClaimedVerifiedStatus
1RetinaNetR (miss rate)34.73Unverified
2FCOSR (miss rate)31.89Unverified
3CrowdDetR (miss rate)25.73Unverified
4EGCLR (miss rate)24.84Unverified
#ModelMetricClaimedVerifiedStatus
1CFTAP5078.2Unverified
2CMXAP5068.9Unverified
#ModelMetricClaimedVerifiedStatus
1LSFMMR0.87Unverified
#ModelMetricClaimedVerifiedStatus
1MMPedestronbox mAP79Unverified