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Pedestrian Detection On Caltech

Metrics

Reasonable Miss Rate

Results

Performance results of various models on this benchmark

Paper Title
LDCF24.8Local Decorrelation For Improved Pedestrian Detection
AlexNet23.3Taking a Deeper Look at Pedestrians
TA-CNN20.9Pedestrian Detection aided by Deep Learning Semantic Tasks
Checkerboards+17.1Filtered Channel Features for Pedestrian Detection
NNNF16.20Pedestrian Detection Inspired by Appearance Constancy and Shape Symmetry
Part-level CNN + saliency and bounding box alignment12.4Part-Level Convolutional Neural Networks for Pedestrian Detection Using Saliency and Boundary Box Alignment
CompACT-Deep11.75Learning Complexity-Aware Cascades for Deep Pedestrian Detection
MCF10.40Learning Multilayer Channel Features for Pedestrian Detection
MS-CNN9.95A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection
SA-FastRCNN9.68Scale-aware Fast R-CNN for Pedestrian Detection
FasterRCNN8.7Is Faster R-CNN Doing Well for Pedestrian Detection?
F-DNN+SS8.18Fused DNN: A deep neural network fusion approach to fast and robust pedestrian detection
SDS-RCNN7.36Illuminating Pedestrians via Simultaneous Detection & Segmentation
RPN+BF7.3Is Faster R-CNN Doing Well for Pedestrian Detection?
TFAN6.5Temporal-Context Enhanced Detection of Heavily Occluded Pedestrians
ALFNet6.1Learning Efficient Single-stage Pedestrian Detectors by Asymptotic Localization Fitting
Zhang et al.5.8CityPersons: A Diverse Dataset for Pedestrian Detection
HyperLearner5.5What Can Help Pedestrian Detection?
Zhang et al. *5.1CityPersons: A Diverse Dataset for Pedestrian Detection
RepLoss5.0Repulsion Loss: Detecting Pedestrians in a Crowd
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Pedestrian Detection On Caltech | SOTA | HyperAI