| LSFM |
- Reasonable Miss Rate
- 0.87
- Heavy MR^-2
- 19.5
|
Localized Semantic Feature Mixers for Efficient Pedestrian Detection in Autonomous Driving 2023-01-01 |
연결 없음 |
| F2DNet (extra data) |
- Reasonable Miss Rate
- 1.71
- Heavy MR^-2
- 20.42
|
F2DNet: Fast Focal Detection Network for Pedestrian Detection 2022-03-04 |
코드 1 · 코드 2 |
| Pedestron |
- Reasonable Miss Rate
- 1.76
- Heavy MR^-2
- 25.7
|
Generalizable Pedestrian Detection: The Elephant In The Room 2020-03-19 |
코드 1 |
| F2DNet |
- Reasonable Miss Rate
- 2.2
- Heavy MR^-2
- 38.7
|
F2DNet: Fast Focal Detection Network for Pedestrian Detection 2022-03-04 |
코드 1 · 코드 2 |
| VLPD |
- Reasonable Miss Rate
- 2.3
- Heavy MR^-2
- 37.7
|
VLPD: Context-Aware Pedestrian Detection via Vision-Language Semantic Self-Supervision 2023-04-06 |
코드 1 |
| NMS-Loss |
- Reasonable Miss Rate
- 2.92
|
NMS-Loss: Learning with Non-Maximum Suppression for Crowded Pedestrian Detection 2021-06-04 |
코드 1 |
| FRCNN+FPN-Res50+refined feature map+Crowdhuman |
- Reasonable Miss Rate
- 3.46
|
CrowdHuman: A Benchmark for Detecting Human in a Crowd 2018-04-30 |
코드 1 |
| CSP + CityPersons dataset |
- Reasonable Miss Rate
- 3.8
|
Center and Scale Prediction: Anchor-free Approach for Pedestrian and Face Detection 2019-04-05 |
코드 1 · 코드 2 |
| RepLoss + CityPersons dataset |
- Reasonable Miss Rate
- 4.0
|
Repulsion Loss: Detecting Pedestrians in a Crowd 2017-11-21 |
코드 1 · 코드 2 |
| OR-CNN + CityPersons dataset |
- Reasonable Miss Rate
- 4.1
|
Occlusion-aware R-CNN: Detecting Pedestrians in a Crowd 2018-07-23 |
연결 없음 |
| CSP |
- Reasonable Miss Rate
- 4.5
|
Center and Scale Prediction: Anchor-free Approach for Pedestrian and Face Detection 2019-04-05 |
코드 1 · 코드 2 |
| ALFNet + CityPersons dataset |
- Reasonable Miss Rate
- 4.5
|
Learning Efficient Single-stage Pedestrian Detectors by Asymptotic Localization Fitting 2018-09-01 |
코드 1 |
| RepLoss |
- Reasonable Miss Rate
- 5.0
|
Repulsion Loss: Detecting Pedestrians in a Crowd 2017-11-21 |
코드 1 · 코드 2 |
| Zhang et al. * |
- Reasonable Miss Rate
- 5.1
|
CityPersons: A Diverse Dataset for Pedestrian Detection 2017-02-19 |
코드 1 · 코드 2 |
| HyperLearner |
- Reasonable Miss Rate
- 5.5
|
What Can Help Pedestrian Detection? 2017-05-08 |
연결 없음 |
| Zhang et al. |
- Reasonable Miss Rate
- 5.8
|
CityPersons: A Diverse Dataset for Pedestrian Detection 2017-02-19 |
코드 1 · 코드 2 |
| ALFNet |
- Reasonable Miss Rate
- 6.1
|
Learning Efficient Single-stage Pedestrian Detectors by Asymptotic Localization Fitting 2018-09-01 |
코드 1 |
| TFAN |
- Reasonable Miss Rate
- 6.5
|
Temporal-Context Enhanced Detection of Heavily Occluded Pedestrians 2020-06-01 |
연결 없음 |
| RPN+BF |
- Reasonable Miss Rate
- 7.3
|
Is Faster R-CNN Doing Well for Pedestrian Detection? 2016-07-24 |
연결 없음 |
| SDS-RCNN |
- Reasonable Miss Rate
- 7.36
|
Illuminating Pedestrians via Simultaneous Detection & Segmentation 2017-06-26 |
코드 1 · 코드 2 |
| F-DNN+SS |
- Reasonable Miss Rate
- 8.18
|
Fused DNN: A deep neural network fusion approach to fast and robust pedestrian detection 2016-10-11 |
연결 없음 |
| FasterRCNN |
- Reasonable Miss Rate
- 8.7
|
Is Faster R-CNN Doing Well for Pedestrian Detection? 2016-07-24 |
연결 없음 |
| SA-FastRCNN |
- Reasonable Miss Rate
- 9.68
|
Scale-aware Fast R-CNN for Pedestrian Detection 2015-10-28 |
연결 없음 |
| MS-CNN |
- Reasonable Miss Rate
- 9.95
|
A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection 2016-07-25 |
코드 1 |
| MCF |
- Reasonable Miss Rate
- 10.40
|
Learning Multilayer Channel Features for Pedestrian Detection 2016-03-01 |
연결 없음 |
| CompACT-Deep |
- Reasonable Miss Rate
- 11.75
|
Learning Complexity-Aware Cascades for Deep Pedestrian Detection 2015-07-19 |
연결 없음 |
| Part-level CNN + saliency and bounding box alignment |
- Reasonable Miss Rate
- 12.4
|
Part-Level Convolutional Neural Networks for Pedestrian Detection Using Saliency and Boundary Box Alignment 2018-10-01 |
코드 1 |
| NNNF |
- Reasonable Miss Rate
- 16.20
|
Pedestrian Detection Inspired by Appearance Constancy and Shape Symmetry 2015-11-25 |
연결 없음 |
| Checkerboards+ |
- Reasonable Miss Rate
- 17.1
|
Filtered Channel Features for Pedestrian Detection 2015-01-23 |
연결 없음 |
| TA-CNN |
- Reasonable Miss Rate
- 20.9
|
Pedestrian Detection aided by Deep Learning Semantic Tasks 2014-11-29 |
연결 없음 |