Smart-CCTV Software: Python based security surveillance solution.

Description:

Our project is an innovative Python-based application that harnesses the power of Tkinter and machine learning to enhance public safety. The core objective of this software is to utilize RTSP (Real-Time Streaming Protocol) links from CCTV cameras to detect violent activities and promptly report them to the authorities via WhatsApp messages, including crucial details such as photos and the camera stream's name.

Key Features:

• Real-time Violence Detection: Our software employs advanced machine learning algorithms to analyze live video streams from CCTV cameras in real-time. It has the capability to recognize various forms of violence, including physical altercations, aggressive behavior, and property damage.

• Integration with RTSP Cameras: The application seamlessly integrates with RTSP-enabled CCTV cameras, allowing users to monitor a wide range of locations, from public streets to private properties.

• Automatic Reporting: When a violent activity is detected, the software automatically generates a WhatsApp message to notify the relevant authorities. The message contains a snapshot of the incident, the camera stream's name, and additional information, enabling swift action.

• User-Friendly Interface: The intuitive Tkinter-based user interface makes it easy for users to configure the system, add cameras, and set up notification preferences.

How to use :

• Download the Repository and install all the degenderizes inside the requirements.txt.
• And after that run the software.py file.
• Give name and phone number by clicking on the family emoji.

• Then give the RTSP link of your Stream :

Crime Prevention Impact:

Implementing a real-time CCTV violence detection and reporting system can have a profound impact on crime prevention in a city. Here are some of the significant benefits:

Immediate Response: By detecting violence as it happens and notifying authorities in real-time, law enforcement can respond promptly to incidents, preventing them from escalating further.

Crime Deterrence: The presence of such technology acts as a powerful deterrent to potential criminals. Knowing that their actions will be recorded and reported can discourage violent behavior.

Evidence Collection: The system provides crucial evidence for criminal investigations and prosecutions. This can lead to higher conviction rates and act as a strong deterrent for repeat offenders.

Community Safety: Enhanced public safety fosters a sense of security among residents and visitors, encouraging economic development and tourism.

Efficient Resource Allocation: Law enforcement agencies can allocate their resources more efficiently by prioritizing responses based on the severity of the detected incidents.

Crime Mapping and Analysis: Over time, the data collected by the system can be used to create crime maps and analyze crime trends, enabling authorities to allocate resources proactively and implement targeted crime prevention strategies.

Overall, our Python-based Tkinter application offers a robust and effective solution for enhancing public safety, preventing crime, and creating safer and more secure communities. By harnessing the power of technology and machine learning, we can work towards a safer future for cities and towns worldwide.