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SOTA
Pedestrian Detection
Pedestrian Detection On Citypersons
Pedestrian Detection On Citypersons
Metrics
Heavy MR^-2
Reasonable MR^-2
Small MR^-2
Test Time
Results
Performance results of various models on this benchmark
Columns
Model Name
Heavy MR^-2
Reasonable MR^-2
Small MR^-2
Test Time
Paper Title
TLL
53.6
15.5
-
-
Small-scale Pedestrian Detection Based on Somatic Topology Localization and Temporal Feature Aggregation
FRCNN
-
15.4
25.6
-
CityPersons: A Diverse Dataset for Pedestrian Detection
FRCNN+Seg
-
14.8
22.6
-
CityPersons: A Diverse Dataset for Pedestrian Detection
TLL+MRF
52.0
14.4
-
-
Small-scale Pedestrian Detection Based on Somatic Topology Localization and Temporal Feature Aggregation
RepLoss
56.9
13.2
-
-
Repulsion Loss: Detecting Pedestrians in a Crowd
OR-CNN
55.7
12.8
-
-
Occlusion-aware R-CNN: Detecting Pedestrians in a Crowd
ALFNet
51.9
12.0
19.0
0.27
Learning Efficient Single-stage Pedestrian Detectors by Asymptotic Localization Fitting
CSP (with offset) + ResNet-50
49.3
11.0
16.0
0.33s/img
Center and Scale Prediction: Anchor-free Approach for Pedestrian and Face Detection
NOH-NMS
53.0
10.8
-
-
NOH-NMS: Improving Pedestrian Detection by Nearby Objects Hallucination
FRCNN+FPN-Res50+refined feature map+Crowdhuman
-
10.67
-
-
CrowdHuman: A Benchmark for Detecting Human in a Crowd
Beta R-CNN
47.1
10.6
-
-
Beta R-CNN: Looking into Pedestrian Detection from Another Perspective
NMS-Loss
-
10.08
-
-
NMS-Loss: Learning with Non-Maximum Suppression for Crowded Pedestrian Detection
SOLIDER
39.4
9.7
-
-
Beyond Appearance: a Semantic Controllable Self-Supervised Learning Framework for Human-Centric Visual Tasks
VLPD
43.1
9.4
10.9
-
VLPD: Context-Aware Pedestrian Detection via Vision-Language Semantic Self-Supervision
ACSP
46.3
9.3
-
-
Adapted Center and Scale Prediction: More Stable and More Accurate
F2DNet
32.6
8.7
11.3
0.44s/img
F2DNet: Fast Focal Detection Network for Pedestrian Detection
LSFM
31.9
8.5
8.8
0.18
Localized Semantic Feature Mixers for Efficient Pedestrian Detection in Autonomous Driving
F2DNet (extra data)
26.23
7.8
9.43
0.44s/img
F2DNet: Fast Focal Detection Network for Pedestrian Detection
Pedestron
33.9
7.5
8.0
-
Generalizable Pedestrian Detection: The Elephant In The Room
LSFM (Additional Data)
24.73
6.38
7.90
0.18
Localized Semantic Feature Mixers for Efficient Pedestrian Detection in Autonomous Driving
0 of 22 row(s) selected.
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