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 251–300 of 438 papers

TitleStatusHype
The P-DESTRE: A Fully Annotated Dataset for Pedestrian Detection, Tracking, Re-Identification and Search from Aerial Devices—0
Near-chip Dynamic Vision Filtering for Low-Bandwidth Pedestrian Detection—0
When Autonomous Systems Meet Accuracy and Transferability through AI: A Survey—0
NMS by Representative Region: Towards Crowded Pedestrian Detection by Proposal Pairing—0
Pedestrian Detection with Wearable Cameras for the Blind: A Two-way Perspective—0
Generalizable Pedestrian Detection: The Elephant In The Room—0
An End-to-End Framework for Unsupervised Pose Estimation of Occluded Pedestrians—0
Input Dropout for Spatially Aligned ModalitiesCode0
Measuring the Utilization of Public Open Spaces by Deep Learning: a Benchmark Study at the Detroit Riverfront—0
Dynamic Error-bounded Lossy Compression (EBLC) to Reduce the Bandwidth Requirement for Real-time Vision-based Pedestrian Safety Applications—0
PSC-Net: Learning Part Spatial Co-occurrence for Occluded Pedestrian Detection—0
Virtual to Real adaptation of Pedestrian Detectors—0
The Pedestrian Patterns Dataset—0
Object as Hotspots: An Anchor-Free 3D Object Detection Approach via Firing of Hotspots—0
PMC-GANs: Generating Multi-Scale High-Quality Pedestrian with Multimodal Cascaded GANs—0
Hybrid Channel Based Pedestrian Detection—0
Seek and You Will Find: A New Optimized Framework for Efficient Detection of Pedestrian—0
Coupled Network for Robust Pedestrian Detection with Gated Multi-Layer Feature Extraction and Deformable Occlusion Handling—0
A Shape Transformation-based Dataset Augmentation Framework for Pedestrian Detection—0
Learning to Separate: Detecting Heavily-Occluded Objects in Urban ScenesCode0
Semantic Head Enhanced Pedestrian Detection in a Crowd—0
Occluded Pedestrian Detection with Visible IoU and Box Sign Predictor—0
Gliding vertex on the horizontal bounding box for multi-oriented object detection—0
Active Learning for Deep Detection Neural NetworksCode0
EdgeNet: Balancing Accuracy and Performance for Edge-based Convolutional Neural Network Object Detectors—0
Gated Multi-layer Convolutional Feature Extraction Network for Robust Pedestrian Detection—0
Attribute-aware Pedestrian Detection in a CrowdCode0
Generative Modeling for Small-Data Object DetectionCode0
Mask-Guided Attention Network for Occluded Pedestrian DetectionCode0
Discriminative Feature Transformation for Occluded Pedestrian Detection—0
Semi-Supervised Pedestrian Instance Synthesis and Detection With Mutual Reinforcement—0
WiderPerson: A Diverse Dataset for Dense Pedestrian Detection in the Wild—0
Learning Lightweight Pedestrian Detector with Hierarchical Knowledge Distillation—0
PedHunter: Occlusion Robust Pedestrian Detector in Crowded Scenes—0
Detector With Focus: Normalizing Gradient In Image Pyramid—0
StarNet: Targeted Computation for Object Detection in Point Clouds—0
Segmentation Mask Guided End-to-End Person SearchCode0
Human Extraction and Scene Transition utilizing Mask R-CNN—0
Pedestrian Tracking by Probabilistic Data Association and Correspondence Embeddings—0
Age and gender bias in pedestrian detection algorithms—0
CDPM: Convolutional Deformable Part Models for Semantically Aligned Person Re-identification—0
Smart IoT Cameras for Crowd Analysis based on augmentation for automatic pedestrian detection, simulation and annotation—0
High-Level Semantic Feature Detection: A New Perspective for Pedestrian DetectionCode0
Distant Pedestrian Detection in the Wild using Single Shot Detector with Deep Convolutional Generative Adversarial Networks—0
Robust Real-time Pedestrian Detection in Aerial Imagery on Jetson TX2—0
Object Detection in 20 Years: A SurveyCode0
Forecasting Pedestrian Trajectory with Machine-Annotated Training DataCode0
Automatic Dataset Augmentation Using Virtual Human Simulation—0
Pedestrian Detection in Thermal Images using Saliency MapsCode0
Automatic adaptation of object detectors to new domains using self-trainingCode0
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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