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
Birds Eye View Social Distancing Analysis System—0
Booster-SHOT: Boosting Stacked Homography Transformations for Multiview Pedestrian Detection with Attention—0
Boosting-like Deep Learning For Pedestrian Detection—0
Box-level Segmentation Supervised Deep Neural Networks for Accurate and Real-time Multispectral Pedestrian Detection—0
Box Re-Ranking: Unsupervised False Positive Suppression for Domain Adaptive Pedestrian Detection—0
Bright Channel Prior Attention for Multispectral Pedestrian Detection—0
CAFF-DINO: Multi-spectral object detection transformers with cross-attention features fusion—0
Cascaded information enhancement and cross-modal attention feature fusion for multispectral pedestrian detection—0
CDPM: Convolutional Deformable Part Models for Semantically Aligned Person Re-identification—0
CHAMP: Crowdsourced, History-Based Advisory of Mapped Pedestrians for Safer Driver Assistance Systems—0
Characterisation of CMOS Image Sensor Performance in Low Light Automotive Applications—0
CircleNet: Reciprocating Feature Adaptation for Robust Pedestrian Detection—0
Combining LiDAR Space Clustering and Convolutional Neural Networks for Pedestrian Detection—0
Comparative study of multi-person tracking methods—0
Comparing Apples and Oranges: Off-Road Pedestrian Detection on the NREC Agricultural Person-Detection Dataset—0
Comparing Computing Platforms for Deep Learning on a Humanoid Robot—0
Computer vision in automated parking systems: Design, implementation and challenges—0
Confidence-Rated Multiple Instance Boosting for Object Detection—0
Constraint-Aware Deep Neural Network Compression—0
Context-Sensitive Decision Forests for Object Detection—0
Convolutional Neural Networks for Aerial Multi-Label Pedestrian Detection—0
Coupled Network for Robust Pedestrian Detection with Gated Multi-Layer Feature Extraction and Deformable Occlusion Handling—0
Cross-Modal Analysis of Human Detection for Robotics: An Industrial Case Study—0
Cross-Modality Proposal-guided Feature Mining for Unregistered RGB-Thermal Pedestrian Detection—0
Data Augmentation in Human-Centric Vision—0
Data-Driven but Privacy-Conscious: Pedestrian Dataset De-identification via Full-Body Person Synthesis—0
DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object Detection—0
Deep Learning based Pedestrian Detection at Distance in Smart Cities—0
Deep Learning Strong Parts for Pedestrian Detection—0
Deep Multi-Task Networks For Occluded Pedestrian Pose Estimation—0
DeepSetNet: Predicting Sets with Deep Neural Networks—0
Detector With Focus: Normalizing Gradient In Image Pyramid—0
Diffusion Dataset Generation: Towards Closing the Sim2Real Gap for Pedestrian Detection—0
DINF: Dynamic Instance Noise Filter for Occluded Pedestrian Detection—0
Discriminative Feature Transformation for Occluded Pedestrian Detection—0
Distance Estimation in Outdoor Driving Environments Using Phase-only Correlation Method with Event Cameras—0
Distant Pedestrian Detection in the Wild using Single Shot Detector with Deep Convolutional Generative Adversarial Networks—0
DMRNet++: Learning Discriminative Features with Decoupled Networks and Enriched Pairs for One-Step Person Search—0
Dynamic Error-bounded Lossy Compression (EBLC) to Reduce the Bandwidth Requirement for Real-time Vision-based Pedestrian Safety Applications—0
EdgeNet: Balancing Accuracy and Performance for Edge-based Convolutional Neural Network Object Detectors—0
Efficient and Robust Pedestrian Detection using Deep Learning for Human-Aware Navigation—0
Efficient Learning of Pinball TWSVM using Privileged Information and its applications—0
Efficient pedestrian detection by directly optimize the partial area under the ROC curve—0
Efficient Pedestrian Detection in Top-View Fisheye Images Using Compositions of Perspective View Patches—0
Embracing Single Stride 3D Object Detector with Sparse Transformer—0
End-to-End Detection and Re-identification Integrated Net for Person Search—0
End-to-end Person Search Sequentially Trained on Aggregated Dataset—0
Enhanced Cooperative Perception for Autonomous Vehicles Using Imperfect Communication—0
Enhanced Multi-View Pedestrian Detection Using Probabilistic Occupancy Volume—0
Enhancing Multi-View Pedestrian Detection Through Generalized 3D Feature Pulling—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