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 76–100 of 438 papers

Title	Date	Tasks	Status	Score
Object Detection in 20 Years: A Survey	May 13, 2019	Face DetectionObject	CodeCode Available	5
Part-Level Convolutional Neural Networks for Pedestrian Detection Using Saliency and Boundary Box Alignment	Oct 1, 2018	Pedestrian Detection	CodeCode Available	5
An Energy-Aware Approach to Design Self-Adaptive AI-based Applications on the Edge	Aug 31, 2023	Heuristic Searchobject-detection	CodeCode Available	5
Accurate Single Stage Detector Using Recurrent Rolling Convolution	Apr 19, 2017	3D Object DetectionObject	CodeCode Available	5
MVUDA: Unsupervised Domain Adaptation for Multi-view Pedestrian Detection	Dec 5, 2024	Domain AdaptationPedestrian Detection	CodeCode Available	5
PedDet: Adaptive Spectral Optimization for Multimodal Pedestrian Detection	Feb 19, 2025	Pedestrian Detection	CodeCode Available	5
MOTChallenge 2015: Towards a Benchmark for Multi-Target Tracking	Apr 8, 2015	3D ReconstructionMultiple Object Tracking	CodeCode Available	5
Multispectral Pedestrian Detection via Simultaneous Detection and Segmentation	Aug 14, 2018	Autonomous DrivingMultispectral Object Detection	CodeCode Available	5
Benchmarking person re-identification datasets and approaches for practical real-world implementations	Dec 20, 2022	BenchmarkingPedestrian Detection	CodeCode Available	5
Bias Behind the Wheel: Fairness Testing of Autonomous Driving Systems	Aug 5, 2023	Autonomous DrivingFairness	CodeCode Available	5
Attribute-aware Pedestrian Detection in a Crowd	Oct 21, 2019	AttributePedestrian Detection	CodeCode Available	5
Analysis of pedestrian activity before and during COVID-19 lockdown, using webcam time-lapse from Cracow and machine learning	Oct 5, 2020	Pedestrian Detection	CodeCode Available	5
Mask-Guided Attention Network for Occluded Pedestrian Detection	Oct 14, 2019	Pedestrian Detection	CodeCode Available	5
Pedestrian Behavior Maps for Safety Advisories: CHAMP Framework and Real-World Data Analysis	May 8, 2023	Pedestrian Detection	CodeCode Available	5
Automatic adaptation of object detectors to new domains using self-training	Apr 15, 2019	Domain AdaptationKnowledge Distillation	CodeCode Available	5
Learning to Separate: Detecting Heavily-Occluded Objects in Urban Scenes	Dec 3, 2019	object-detectionObject Detection	CodeCode Available	5
Learning Scene-Pedestrian Graph for End to end Person Search	Aug 9, 2023	Graph MatchingPedestrian Detection	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
Learning Cross-Modal Deep Representations for Robust Pedestrian Detection	Apr 8, 2017	Pedestrian Detection	CodeCode Available	5
Convolutional Channel Features	Apr 28, 2015	Edge DetectionFace Detection	CodeCode Available	5
Joint Detection and Identification Feature Learning for Person Search	Apr 7, 2016	Pedestrian DetectionPerson Re-Identification	CodeCode Available	5
Forecasting Pedestrian Trajectory with Machine-Annotated Training Data	May 9, 2019	Autonomous VehiclesPedestrian Detection	CodeCode Available	5
Learning Efficient Single-stage Pedestrian Detectors by Asymptotic Localization Fitting	Sep 1, 2018	Pedestrian Detection	CodeCode Available	5
Linear Support Tensor Machine: Pedestrian Detection in Thermal Infrared Images	Sep 26, 2016	AttributePedestrian Detection	CodeCode Available	5

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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