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Monocular Depth Estimation using a Multi-grid Attention-based Model
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Speedy Image Crowd Counting by Light Weight Convolutional Neural Network
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Construction of Efficient Smart Voting Machine with Liveness Detection Module
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An Economical Robotic Arm for Playing Chess Using Visual Servoing
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Triplet loss for Chromosome Classification
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Unstructured Noise Removal for Industrial Sensor Imaging Unit by Hybrid Adaptive Median Algorithm
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Analysis of Artificial Intelligence based Image Classification Techniques
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A REVIEW ON IOT BASED MEDICAL IMAGING TECHNOLOGY FOR HEALTHCARE APPLICATIONS
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COMPUTER VISION BASED TRAFFIC SIGN SENSING FOR SMART TRANSPORT
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Diabetic Retinopathy Detection Using Machine Learning
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Deep Learning based Handwriting Recognition with Adversarial Feature Deformation and Regularization
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State of Art Survey on Plant Leaf Disease Detection
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Optimal Compression of Remote Sensing Images Using Deep Learning during Transmission of Data
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OverFeat Network Algorithm for Fabric Defect Detection in Textile Industry
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VIRTUAL RESTORATION OF DAMAGED ARCHEOLOGICAL ARTIFACTS OBTAINED FROM EXPEDITIONS USING 3D VISUALIZATION
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Two-Stage Frame Extraction in Video Analysis for Accurate Prediction of Object Tracking by Improved Deep Learning
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Volume - 3 | Issue - 3 | september 2021

Speedy Image Crowd Counting by Light Weight Convolutional Neural Network
Pages: 208-222
Published
08 September, 2021
Abstract

In image/video analysis, crowds are actively researched, and their numbers are counted. In the last two decades, many crowd counting algorithms have been developed for a wide range of applications in crisis management systems, large-scale events, workplace safety, and other areas. The precision of neural network research for estimating points is outstanding in computer vision domain. However, the degree of uncertainty in the estimate is rarely indicated. Point estimate is beneficial for measuring uncertainty since it can improve the quality of decisions and predictions. The proposed framework integrates Light weight CNN (LW-CNN) for implementing crowd computing in any public place for delivering higher accuracy in counting. Further, the proposed framework has been trained through various scene analysis such as the full and partial vision of heads in counting. Based on the various scaling sets in the proposed neural network framework, it can easily categorize the partial vision of heads count and it is being counted accurately than other pre-trained neural network models. The proposed framework provides higher accuracy in estimating the headcounts in public places during COVID-19 by consuming less amount of time.

Keywords

CNN Crowd counting COVID 19

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