Head Detection
14 papers with code • 1 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in Head Detection
| Trend | Dataset | Best Model | Paper | Code | Compare |
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MinneApple
SCUT-HEAD
Most implemented papers
Global Wheat Head Detection (GWHD) dataset: a large and diverse dataset of high resolution RGB labelled images to develop and benchmark wheat head detection methods
p2irc/UDA4POC
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Detection of wheat heads is an important task allowing to estimate pertinent traits including head population density and head characteristics such as sanitary state, size, maturity stage and the presence of awns.
Tracking Pedestrian Heads in Dense Crowd
Sentient07/HeadHunter--T
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CVPR 2021
Moreover, we also propose a new head detector, HeadHunter, which is designed for small head detection in crowded scenes.
FCHD: Fast and accurate head detection in crowded scenes
aditya-vora/FCHD-Fully-Convolutional-Head-Detector
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In this paper, we propose FCHD-Fully Convolutional Head Detector, an end-to-end trainable head detection model.
Context-aware CNNs for person head detection
aosokin/cnn_head_detection
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ICCV 2015
First, we leverage person-scene relations and propose a Global CNN model trained to predict positions and scales of heads directly from the full image.
Relational Learning for Joint Head and Human Detection
Head and human detection have been rapidly improved with the development of deep convolutional neural networks.
RefineDetLite: A Lightweight One-stage Object Detection Framework for CPU-only Devices
2023-MindSpore-1/ms-code-216
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Therefore, high efficiency object detectors on CPU-only devices are urgently-needed in industry.
Towards in-store multi-person tracking using head detection and track heatmaps
In addition, we describe an illustrative example of the use of this dataset for tracking participants based on a head tracking model in an effort to minimize errors due to occlusion.
DPDnet: A Robust People Detector using Deep Learning with an Overhead Depth Camera
lehommee/DPDnet-A-robust-people-detector-using-deep-learning-with-an-overhead-depth-camera
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In this paper we propose a method based on deep learning that detects multiple people from a single overhead depth image with high reliability.
Weakly supervised one-stage vision and language disease detection using large scale pneumonia and pneumothorax studies
The architectural modifications address three obstacles -- implementing a supervised vision and language detection method in a weakly supervised fashion, incorporating clinical referring expression natural language information, and generating high fidelity detections with map probabilities.
Towards Resolving the Challenge of Long-tail Distribution in UAV Images for Object Detection
we1pingyu/DSHNet
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To this end, we rethink long-tailed object detection in UAV images and propose the Dual Sampler and Head detection Network (DSHNet), which is the first work that aims to resolve long-tail distribution in UAV images.
