Developed Counting Convolutional Neural Network (CCNN) for Crowd Counting- Deep Neural Network Course Project
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Updated
Nov 1, 2022 - Jupyter Notebook
Developed Counting Convolutional Neural Network (CCNN) for Crowd Counting- Deep Neural Network Course Project
This machine learning project uses computer vision techniques to count the number of people entering and exiting a mall.
This repository performs crowd counting inference using a pre-trained ONNX model. Input an image to estimate head localization in crowded scenes.
GCC dataset Collector and Labeler (GCC CL) [CVPR2019]
Using transfer learning on pretrained image models to learn density map generation and count the number of people in an image.
A modified version of OpenLTE able to extract Channel State Information (CSI) from LTE signals.
ComPtr: Towards Diverse Bi-source Dense Prediction Tasks via A Simple yet General Complementary Transformer
This is the implementation of paper "A Multi-Scale and Multi-level Feature Aggregation Network for Crowd Counting"
SOFT-CSRNET : Counting people in drone video footage
Crowd counting on the ShanghaiTech dataset, using multi-column convolutional neural networks.
A modified version of the LTE Scanner supporting RTL-SDR/HackRF/BladeRF and able to extract Channel State Information (CSI) from LTE signals.
CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes
Multi-level Attention Refined UNet for crowd counting
LWCC: A LightWeight Crowd Counting library for Python that includes several pretrained state-of-the-art models.
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