Diabetic Retinopathy Detection Using Machine Learning
Volume-4 | Issue-1
Monocular Depth Estimation using a Multi-grid Attention-based Model
Volume-4 | Issue-3
Speedy Image Crowd Counting by Light Weight Convolutional Neural Network
Volume-3 | Issue-3
Construction of Efficient Smart Voting Machine with Liveness Detection Module
Volume-3 | Issue-3
An Economical Robotic Arm for Playing Chess Using Visual Servoing
Volume-2 | Issue-3
Triplet loss for Chromosome Classification
Volume-4 | Issue-1
Unstructured Noise Removal for Industrial Sensor Imaging Unit by Hybrid Adaptive Median Algorithm
Volume-3 | Issue-4
Real Time Sign Language Recognition and Speech Generation
Volume-2 | Issue-2
Analysis of Artificial Intelligence based Image Classification Techniques
Volume-2 | Issue-1
Design of ANN Based Machine Learning Method for Crop Prediction
Volume-3 | Issue-3
A REVIEW ON IOT BASED MEDICAL IMAGING TECHNOLOGY FOR HEALTHCARE APPLICATIONS
Volume-1 | Issue-1
COMPUTER VISION BASED TRAFFIC SIGN SENSING FOR SMART TRANSPORT
Volume-1 | Issue-1
Diabetic Retinopathy Detection Using Machine Learning
Volume-4 | Issue-1
Accurate Segmentation for Low Resolution Satellite images by Discriminative Generative Adversarial Network for Identifying Agriculture Fields
Volume-3 | Issue-4
Deep Learning based Handwriting Recognition with Adversarial Feature Deformation and Regularization
Volume-3 | Issue-4
State of Art Survey on Plant Leaf Disease Detection
Volume-4 | Issue-2
Optimal Compression of Remote Sensing Images Using Deep Learning during Transmission of Data
Volume-3 | Issue-4
OverFeat Network Algorithm for Fabric Defect Detection in Textile Industry
Volume-3 | Issue-4
VIRTUAL RESTORATION OF DAMAGED ARCHEOLOGICAL ARTIFACTS OBTAINED FROM EXPEDITIONS USING 3D VISUALIZATION
Volume-1 | Issue-2
Two-Stage Frame Extraction in Video Analysis for Accurate Prediction of Object Tracking by Improved Deep Learning
Volume-3 | Issue-4
Volume - 5 | Issue - 3 | september 2023
Published
01 August, 2023
This research focuses on developing a reliable computer vision system for accurately tracking traffic density in India during the rainy season. The system uses deep learning-based techniques to handle the difficulties associated with vehicle detection and tracking. The three modules are vehicle detection, tracking, and vehicle counting. Vehicles are initially identified using the YOLOv8 algorithm, a state-of-the-art deep learning detector. Subsequently, the DeepSORT algorithm is utilized for multi-object tracking to ensure accurate and robust tracking of various objects, including cars, buses, trucks, bikes, and pedestrians. The importance of accurate vehicle counting and speed measurement is emphasized, especially during bad weather. An independently compiled dataset of Indian rainy conditions is used to assess the proposed computer vision system. The outcomes demonstrate the system's capability to accurately identify, track, count, and estimate the speeds of vehicles. These features offer insightful information for traffic analysis, including flow monitoring, congestion detection, and other associated traffic challenges. This study makes a contribution to the field of computer vision-based traffic monitoring and offers potential applications in transportation management systems under challenging weather conditions.
KeywordsTraffic density tracking Vehicle detection Vehicle tracking Vehicle counting YOLOv8 algorithm DeepSORT Algorithm
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