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 Added Most Hyped Most Active Needs Verification Most Verified

Showing 101–125 of 438 papers

Title	Date	Tasks	Status	Score
Multispectral Pedestrian Detection via Simultaneous Detection and Segmentation	Aug 14, 2018	Autonomous DrivingMultispectral Object Detection	CodeCode Available	5
Deep convolutional neural networks for pedestrian detection	Oct 13, 2015	image-classificationImage Classification	CodeCode Available	5
Towards Pedestrian Detection Using RetinaNet in ECCV 2018 Wider Pedestrian Detection Challenge	Feb 4, 2019	General Classificationimage-classification	CodeCode Available	5
Automatic adaptation of object detectors to new domains using self-training	Apr 15, 2019	Domain AdaptationKnowledge Distillation	CodeCode Available	5
A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection	Jul 25, 2016	Face DetectionFeature Upsampling	CodeCode Available	5
Count- and Similarity-aware R-CNN for Pedestrian Detection	Aug 1, 2020	Human Instance SegmentationInstance Segmentation	CodeCode Available	5
Convolutional Channel Features	Apr 28, 2015	Edge DetectionFace Detection	CodeCode Available	5
Mask-Guided Attention Network for Occluded Pedestrian Detection	Oct 14, 2019	Pedestrian Detection	CodeCode Available	5
MOTChallenge 2015: Towards a Benchmark for Multi-Target Tracking	Apr 8, 2015	3D ReconstructionMultiple Object Tracking	CodeCode Available	5
Continual Learning for Out-of-Distribution Pedestrian Detection	Jun 26, 2023	Continual Learningobject-detection	CodeCode Available	5
Learning to Separate: Detecting Heavily-Occluded Objects in Urban Scenes	Dec 3, 2019	object-detectionObject Detection	CodeCode Available	5
Learning Efficient Single-stage Pedestrian Detectors by Asymptotic Localization Fitting	Sep 1, 2018	Pedestrian Detection	CodeCode Available	5
Active Learning for Deep Detection Neural Networks	Nov 20, 2019	Active LearningPedestrian Detection	CodeCode Available	5
Generalizable Pedestrian Detection: The Elephant In The Room	Mar 19, 2020	Autonomous DrivingPedestrian Detection	CodeCode Available	5
Learning Scene-Pedestrian Graph for End to end Person Search	Aug 9, 2023	Graph MatchingPedestrian Detection	CodeCode Available	5
Linear Support Tensor Machine: Pedestrian Detection in Thermal Infrared Images	Sep 26, 2016	AttributePedestrian Detection	CodeCode Available	5
Confidence-aware Fusion using Dempster-Shafer Theory for Multispectral Pedestrian Detection	Mar 21, 2022	Multispectral Object DetectionPedestrian Detection	CodeCode Available	5
PFSD: A Multi-Modal Pedestrian-Focus Scene Dataset for Rich Tasks in Semi-Structured Environments	Feb 21, 2025	AttributeAutonomous Driving	CodeCode Available	5
Joint Detection and Identification Feature Learning for Person Search	Apr 7, 2016	Pedestrian DetectionPerson Re-Identification	CodeCode Available	5
Input Dropout for Spatially Aligned Modalities	Feb 7, 2020	Object TrackingPedestrian Detection	CodeCode Available	5
Learning Cross-Modal Deep Representations for Robust Pedestrian Detection	Apr 8, 2017	Pedestrian Detection	CodeCode Available	5
Robustness Analysis of Pedestrian Detectors for Surveillance	Jul 12, 2018	Pedestrian Detection	CodeCode Available	5
Illuminating Pedestrians via Simultaneous Detection & Segmentation	Jun 26, 2017	Autonomous DrivingPedestrian Detection	CodeCode Available	5
Improved Instance Discrimination and Feature Compactness for End-to-End Person Search	May 21, 2021	Pedestrian DetectionPerson Re-Identification	CodeCode Available	5
Gliding vertex on the horizontal bounding box for multi-oriented object detection	Nov 21, 2019	Objectobject-detection	CodeCode Available	5

Show:10 25 50

← PrevPage 5 of 18Next →

All datasets Caltech CityPersons LLVIP DVTOD TJU-Ped-traffic TJU-Ped-campus CVC14 Caltech Pedestrian Dataset MMPD-Dataset

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	UniHCP (FT)	Heavy MR^-2	27.2	—	Unverified
2	LDCF	Reasonable Miss Rate	24.8	—	Unverified
3	AlexNet	Reasonable Miss Rate	23.3	—	Unverified
4	TA-CNN	Reasonable Miss Rate	20.9	—	Unverified
5	Checkerboards+	Reasonable Miss Rate	17.1	—	Unverified
6	NNNF	Reasonable Miss Rate	16.2	—	Unverified
7	Part-level CNN + saliency and bounding box alignment	Reasonable Miss Rate	12.4	—	Unverified
8	CompACT-Deep	Reasonable Miss Rate	11.75	—	Unverified
9	MCF	Reasonable Miss Rate	10.4	—	Unverified
10	MS-CNN	Reasonable Miss Rate	9.95	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ACSP + EuroCity Persons	Heavy MR^-2	42.5	—	Unverified
2	TLL	Reasonable MR^-2	15.5	—	Unverified
3	FRCNN	Reasonable MR^-2	15.4	—	Unverified
4	FRCNN+Seg	Reasonable MR^-2	14.8	—	Unverified
5	TLL+MRF	Reasonable MR^-2	14.4	—	Unverified
6	RepLoss	Reasonable MR^-2	13.2	—	Unverified
7	OR-CNN	Reasonable MR^-2	12.8	—	Unverified
8	ALFNet	Reasonable MR^-2	12	—	Unverified
9	CSP (with offset) + ResNet-50	Reasonable MR^-2	11	—	Unverified
10	NOH-NMS	Reasonable MR^-2	10.8	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	INSANet	log average miss rate	4.43	—	Unverified
2	MMPedestron	AP	0.73	—	Unverified
3	CAFF-DINO	AP	0.69	—	Unverified
4	CFT	AP	0.64	—	Unverified
5	UniRGB-IR	AP	0.63	—	Unverified
6	RSDet	AP	0.61	—	Unverified
7	CMX	AP	0.6	—	Unverified
8	CSSA	AP	0.59	—	Unverified
9	GAFF	AP	0.56	—	Unverified
10	Halfway Fusion	AP	0.55	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	YOLOv6 (Thermal)	mAP	84.4	—	Unverified
2	CFT	mAP	82.7	—	Unverified
3	YOLOv3 (Thermal)	mAP	82.7	—	Unverified
4	CMX	mAP	81.6	—	Unverified
5	YOLOv7 (Thermal)	mAP	77.8	—	Unverified
6	YOLOv6 (Visible)	mAP	38.1	—	Unverified
7	YOLOv7 (Visible)	mAP	35.3	—	Unverified
8	YOLOv3 (Visible)	mAP	34.5	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FCOS	R (miss rate)	24.35	—	Unverified
2	RetinaNet	R (miss rate)	23.89	—	Unverified
3	FPN	R (miss rate)	22.3	—	Unverified
4	CrowdDet	R (miss rate)	20.82	—	Unverified
5	EGCL	R (miss rate)	19.73	—	Unverified
6	LSFM	R (miss rate)	18.7	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	RetinaNet	R (miss rate)	34.73	—	Unverified
2	FCOS	R (miss rate)	31.89	—	Unverified
3	CrowdDet	R (miss rate)	25.73	—	Unverified
4	EGCL	R (miss rate)	24.84	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CFT	AP50	78.2	—	Unverified
2	CMX	AP50	68.9	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	LSFM	MR	0.87	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MMPedestron	box mAP	79	—	Unverified