Pedestrian Detection On Caltech

评估指标

Reasonable Miss Rate

评测结果

各个模型在此基准测试上的表现结果

		Paper Title
LDCF	24.8	Local Decorrelation For Improved Pedestrian Detection
AlexNet	23.3	Taking a Deeper Look at Pedestrians
TA-CNN	20.9	Pedestrian Detection aided by Deep Learning Semantic Tasks
Checkerboards+	17.1	Filtered Channel Features for Pedestrian Detection
NNNF	16.20	Pedestrian Detection Inspired by Appearance Constancy and Shape Symmetry
Part-level CNN + saliency and bounding box alignment	12.4	Part-Level Convolutional Neural Networks for Pedestrian Detection Using Saliency and Boundary Box Alignment
CompACT-Deep	11.75	Learning Complexity-Aware Cascades for Deep Pedestrian Detection
MCF	10.40	Learning Multilayer Channel Features for Pedestrian Detection
MS-CNN	9.95	A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection
SA-FastRCNN	9.68	Scale-aware Fast R-CNN for Pedestrian Detection
FasterRCNN	8.7	Is Faster R-CNN Doing Well for Pedestrian Detection?
F-DNN+SS	8.18	Fused DNN: A deep neural network fusion approach to fast and robust pedestrian detection
SDS-RCNN	7.36	Illuminating Pedestrians via Simultaneous Detection & Segmentation
RPN+BF	7.3	Is Faster R-CNN Doing Well for Pedestrian Detection?
TFAN	6.5	Temporal-Context Enhanced Detection of Heavily Occluded Pedestrians
ALFNet	6.1	Learning Efficient Single-stage Pedestrian Detectors by Asymptotic Localization Fitting
Zhang et al.	5.8	CityPersons: A Diverse Dataset for Pedestrian Detection
HyperLearner	5.5	What Can Help Pedestrian Detection?
Zhang et al. *	5.1	CityPersons: A Diverse Dataset for Pedestrian Detection
RepLoss	5.0	Repulsion Loss: Detecting Pedestrians in a Crowd

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Pedestrian Detection On Caltech

评估指标

评测结果