Hetul16
commented
6 months ago Solution: To address person detection on railway tracks, we can either propose CNN from scratch or leverage a transfer learning approach with pre-trained models like YOLOv5 or ResNet50, employing OpenCV for robust implementation. Our approach involves training on a curated dataset specific to this project, fine-tuning models for accurate detection. Additionally, integrating an alert mechanism ensures timely notifications to authorities upon detecting individuals on tracks.
Why Me: I am GSSOC'24 Contributor With deep expertise in CNNs and computer vision, I am well-suited for this project. My experience encompasses both theoretical understanding and practical application, as demonstrated by presentations and project work. I bring a blend of skills, including model development and insights from research (I have presented a research paper in the area of convolutional neural networks), making me confident in contributing effectively to this endeavor.
richardar
TAHIR0110
tejaswini4996
Dhwanika26
Problem: In recent years, there has been a concerning rise in railway-related suicides. Detecting individuals on railway tracks in real time to prevent such incidents is a critical challenge. Existing methods often rely on human intervention, which may not always be timely or effective.
Solution: I propose implementing a feature within the existing railway safety systems to detect individuals on railway tracks using an advanced computer vision algorithm. This feature would analyze live camera feeds along railway tracks and identify human presence, alerting railway authorities or relevant emergency services promptly.
I am gssoc contributor, kindly assign this to me.