AI-Based Intelligent Camera Decision-Making System

Problem Statement

This project aims to design and implement an AI-based intelligent camera decision-making system to address challenges in traditional surveillance methods. The goal is to process video data, identify crucial events, and make real-time informed decisions. The system addresses issues such as human fatigue, delayed responses, and the need for continuous monitoring.

Solution

Integrating Pre-installed Security Cameras

A cost-effective approach leveraging existing infrastructure.
Upgrade camera firmware for compatibility.
Implement adaptive machine learning for continuous improvement.

Processing Real-time Video Data via Media Pipes

Utilizing the Mediapipe framework for face and body pose recognition, hand tracking, etc.
Implementing deep learning models (CNN) for object (arms) detection.
Object detection using OpenCV.

Triggers for Suspicious Activity on Authorized Person’s Handheld Device

Mobile app integration for instant updates on suspicious activity.
Location, photo, and timestamp of the incident provided with the alert.
Email alerts for maintaining a record of the activity.

Re-assurance in Case of Misjudged Decision

Photo proof sent along with the alert to avoid misjudgments.

Solution Implementation

Hardware and Infrastructure Setup

High-resolution cameras for advanced video capture.
GPU for accelerated deep learning algorithms.
Multicore CPU, sufficient RAM, and high-speed storage.
Robust network infrastructure for seamless data transfer.

Software and Technology

Programming Languages: Python, C++, Kotlin
Frameworks: TensorFlow, PyTorch, CNN, LSTM, SVM, etc.
Tools: OpenCV for computer vision, Mediapipe for body postures and hand tracking
Cloud Services: Google Cloud Platform

Team Members & Responsibilities

Kshitiz Agrawal: Integration of mobile and sensors for data collection.
Aarohi Saxena: Machine Learning & Research.
Rachit Goyal: Sensors integration and IoT.

Conclusion

The proposed AI-based intelligent camera decision-making system overcomes limitations in traditional surveillance methods, offering real-time analysis, quick decision-making, and continuous monitoring. The interdisciplinary approach makes it suitable for integration in police stations, empowering authorities with quick and authentic crime case information.

Graphical Representation

This README provides an overview of the AI-based system, its components, and the team's responsibilities. For a detailed guide on installation and usage, please refer to the project documentation.

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