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 51–100 of 438 papers

TitleStatusHype
Anchor-free Small-scale Multispectral Pedestrian DetectionCode1
HalluciDet: Hallucinating RGB Modality for Person Detection Through Privileged InformationCode1
Cross-Modality Fusion Transformer for Multispectral Object DetectionCode1
AMFD: Distillation via Adaptive Multimodal Fusion for Multispectral Pedestrian DetectionCode1
LMOT: Efficient Light-Weight Detection and Tracking in CrowdsCode1
Causal Mode Multiplexer: A Novel Framework for Unbiased Multispectral Pedestrian DetectionCode1
Body-Face Joint Detection via Embedding and Head HookCode1
NMS-Loss: Learning with Non-Maximum Suppression for Crowded Pedestrian DetectionCode1
Multispectral Deep Neural Networks for Pedestrian DetectionCode1
Bounding Box Stability against Feature Dropout Reflects Detector Generalization across EnvironmentsCode1
CARLA-BSP: a simulated dataset with pedestriansCode1
Comparison Of Deep Object Detectors On A New Vulnerable Pedestrian DatasetCode1
Domain Adaptive Person SearchCode1
DETR for Crowd Pedestrian DetectionCode1
Efficient and Effective Generation of Test Cases for Pedestrian Detection -- Search-based Software Testing of Baidu Apollo in SVLCode1
Bringing Generalization to Deep Multi-View Pedestrian DetectionCode1
MambaST: A Plug-and-Play Cross-Spectral Spatial-Temporal Fuser for Efficient Pedestrian DetectionCode1
DDAM-PS: Diligent Domain Adaptive Mixer for Person SearchCode1
Exploring Visual Context for Weakly Supervised Person SearchCode1
FCOS: Fully Convolutional One-Stage Object DetectionCode1
From Handcrafted to Deep Features for Pedestrian Detection: A SurveyCode1
Guided Attentive Feature Fusion for Multispectral Pedestrian DetectionCode1
MiPa: Mixed Patch Infrared-Visible Modality Agnostic Object DetectionCode1
CityPersons: A Diverse Dataset for Pedestrian DetectionCode1
Untargeted Backdoor Attack against Object DetectionCode1
Box Re-Ranking: Unsupervised False Positive Suppression for Domain Adaptive Pedestrian Detection—0
Box-level Segmentation Supervised Deep Neural Networks for Accurate and Real-time Multispectral Pedestrian Detection—0
An Objective Method for Pedestrian Occlusion Level Classification—0
Boosting-like Deep Learning For Pedestrian Detection—0
An FPGA-Accelerated Design for Deep Learning Pedestrian Detection in Self-Driving Vehicles—0
Adversarial Attacks on Event-Based Pedestrian Detectors: A Physical Approach—0
Booster-SHOT: Boosting Stacked Homography Transformations for Multiview Pedestrian Detection with Attention—0
Adaptive NMS: Refining Pedestrian Detection in a Crowd—0
Birds Eye View Social Distancing Analysis System—0
An End-to-End Framework for Unsupervised Pose Estimation of Occluded Pedestrians—0
Evaluating and Enhancing Trustworthiness of LLMs in Perception Tasks—0
Distant Pedestrian Detection in the Wild using Single Shot Detector with Deep Convolutional Generative Adversarial Networks—0
Bi-box Regression for Pedestrian Detection and Occlusion Estimation—0
Beyond Domain Adaptation: Unseen Domain Encapsulation via Universal Non-volume Preserving Models—0
An Efficient Edge Detection Approach to Provide Better Edge Connectivity for Image Analysis—0
Beta R-CNN: Looking into Pedestrian Detection from Another Perspective—0
Adaptive Feature Fusion for Cooperative Perception using LiDAR Point Clouds—0
Benchmarking person re-identification approaches and training datasets for practical real-world implementations—0
Data-Driven but Privacy-Conscious: Pedestrian Dataset De-identification via Full-Body Person Synthesis—0
6G Integrated Sensing and Communication: From Vision to Realization—0
Data Augmentation in Human-Centric Vision—0
Basis Mapping Based Boosting for Object Detection—0
Diffusion Dataset Generation: Towards Closing the Sim2Real Gap for Pedestrian Detection—0
BAANet: Learning Bi-directional Adaptive Attention Gates for Multispectral Pedestrian Detection—0
Adaptive Algorithm and Platform Selection for Visual Detection and Tracking—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