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
Box Re-Ranking: Unsupervised False Positive Suppression for Domain Adaptive Pedestrian Detection	Feb 1, 2021	object-detectionObject Detection	—Unverified
Box-level Segmentation Supervised Deep Neural Networks for Accurate and Real-time Multispectral Pedestrian Detection	Feb 14, 2019	Autonomous DrivingComputational Efficiency	—Unverified
An Objective Method for Pedestrian Occlusion Level Classification	May 11, 2022	Autonomous VehiclesClassification	—Unverified
Boosting-like Deep Learning For Pedestrian Detection	May 26, 2015	Deep LearningPedestrian Detection	—Unverified
An FPGA-Accelerated Design for Deep Learning Pedestrian Detection in Self-Driving Vehicles	Sep 16, 2018	object-detectionObject Detection	—Unverified
Adversarial Attacks on Event-Based Pedestrian Detectors: A Physical Approach	Mar 1, 2025	Pedestrian Detection	—Unverified
Booster-SHOT: Boosting Stacked Homography Transformations for Multiview Pedestrian Detection with Attention	Aug 19, 2022	Multiview DetectionPedestrian Detection	—Unverified
Adaptive NMS: Refining Pedestrian Detection in a Crowd	Apr 7, 2019	Object DetectionPedestrian Detection	—Unverified
Birds Eye View Social Distancing Analysis System	Dec 14, 2021	object-detectionObject Detection	—Unverified
An End-to-End Framework for Unsupervised Pose Estimation of Occluded Pedestrians	Feb 15, 2020	Domain AdaptationInstance Segmentation	—Unverified
Evaluating and Enhancing Trustworthiness of LLMs in Perception Tasks	Jul 18, 2024	Hallucinationobject-detection	—Unverified
Distant Pedestrian Detection in the Wild using Single Shot Detector with Deep Convolutional Generative Adversarial Networks	May 29, 2019	object-detectionObject Detection	—Unverified
Bi-box Regression for Pedestrian Detection and Occlusion Estimation	Sep 1, 2018	Occlusion EstimationPedestrian Detection	—Unverified
Beyond Domain Adaptation: Unseen Domain Encapsulation via Universal Non-volume Preserving Models	Dec 9, 2018	Domain AdaptationDomain Generalization	—Unverified
An Efficient Edge Detection Approach to Provide Better Edge Connectivity for Image Analysis	Mar 13, 2019	Edge DetectionFace Detection	—Unverified
Beta R-CNN: Looking into Pedestrian Detection from Another Perspective	Oct 23, 2022	Pedestrian Detection	—Unverified
Adaptive Feature Fusion for Cooperative Perception using LiDAR Point Clouds	Jul 30, 2022	Domain Adaptationfeature selection	—Unverified
Benchmarking person re-identification approaches and training datasets for practical real-world implementations	Sep 29, 2021	BenchmarkingPedestrian Detection	—Unverified
Data-Driven but Privacy-Conscious: Pedestrian Dataset De-identification via Full-Body Person Synthesis	Jun 20, 2023	De-identificationImage Generation	—Unverified
6G Integrated Sensing and Communication: From Vision to Realization	May 3, 2023	Integrated sensing and communicationISAC	—Unverified
Data Augmentation in Human-Centric Vision	Mar 13, 2024	Data AugmentationHuman Parsing	—Unverified
Basis Mapping Based Boosting for Object Detection	Jun 1, 2015	Objectobject-detection	—Unverified
Diffusion Dataset Generation: Towards Closing the Sim2Real Gap for Pedestrian Detection	May 16, 2023	Dataset GenerationPedestrian Detection	—Unverified
BAANet: Learning Bi-directional Adaptive Attention Gates for Multispectral Pedestrian Detection	Dec 4, 2021	Pedestrian DetectionSpecificity	—Unverified
Adaptive Algorithm and Platform Selection for Visual Detection and Tracking	May 21, 2016	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