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 376–400 of 438 papers

Title	Date	Tasks	Status
Fused DNN: A deep neural network fusion approach to fast and robust pedestrian detection	Oct 11, 2016	Pedestrian DetectionSemantic Segmentation	—Unverified
Real-Time RGB-D based Template Matching Pedestrian Detection	Oct 3, 2016	Pedestrian DetectionTemplate Matching	—Unverified
Linear Support Tensor Machine: Pedestrian Detection in Thermal Infrared Images	Sep 26, 2016	AttributePedestrian Detection	CodeCode Available
Reduced Memory Region Based Deep Convolutional Neural Network Detection	Sep 8, 2016	Pedestrian Detection	—Unverified
A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection	Jul 25, 2016	Face DetectionFeature Upsampling	CodeCode Available
Is Faster R-CNN Doing Well for Pedestrian Detection?	Jul 24, 2016	Objectobject-detection	—Unverified
A Real-Time Deep Learning Pedestrian Detector for Robot Navigation	Jul 15, 2016	Deep LearningPedestrian Detection	—Unverified
Efficient and Robust Pedestrian Detection using Deep Learning for Human-Aware Navigation	Jul 15, 2016	Human DetectionPedestrian Detection	—Unverified
LCrowdV: Generating Labeled Videos for Simulation-based Crowd Behavior Learning	Jun 29, 2016	General ClassificationPedestrian Detection	—Unverified
Large Scale Hard Sample Mining With Monte Carlo Tree Search	Jun 1, 2016	Face Detectionobject-detection	—Unverified
Semantic Channels for Fast Pedestrian Detection	Jun 1, 2016	CPUGPU	—Unverified
Adaptive Algorithm and Platform Selection for Visual Detection and Tracking	May 21, 2016	Pedestrian Detection	—Unverified
Person Re-identification in the Wild	Apr 9, 2016	BenchmarkingPedestrian Detection	—Unverified
Joint Detection and Identification Feature Learning for Person Search	Apr 7, 2016	Pedestrian DetectionPerson Re-Identification	CodeCode Available
Pushing the Limits of Deep CNNs for Pedestrian Detection	Mar 15, 2016	Occlusion HandlingOptical Flow Estimation	—Unverified
Learning Multilayer Channel Features for Pedestrian Detection	Mar 1, 2016	Pedestrian Detection	—Unverified
How Far are We from Solving Pedestrian Detection?	Feb 3, 2016	ClusteringPedestrian Detection	—Unverified
Deep Learning Strong Parts for Pedestrian Detection	Dec 1, 2015	Deep LearningOcclusion Handling	—Unverified
Object Detection Using Generalization and Efficiency Balanced Co-Occurrence Features	Dec 1, 2015	feature selectionObject	—Unverified
Pedestrian Detection Inspired by Appearance Constancy and Shape Symmetry	Nov 25, 2015	Pedestrian Detection	—Unverified
A convnet for non-maximum suppression	Nov 19, 2015	ClusteringObject	—Unverified
Scale-aware Fast R-CNN for Pedestrian Detection	Oct 28, 2015	Pedestrian DetectionPhilosophy	—Unverified
Deep convolutional neural networks for pedestrian detection	Oct 13, 2015	image-classificationImage Classification	CodeCode Available
Learning Complexity-Aware Cascades for Deep Pedestrian Detection	Jul 19, 2015	Pedestrian Detection	—Unverified
Multispectral Pedestrian Detection: Benchmark Dataset and Baseline	Jun 1, 2015	Pedestrian Detection	—Unverified

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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