Smart City Cop

Inspiration

With the changing times, the role of women has improved and now along with acting as a perfect home-maker, she is very active in her professional life too. With the increasing participation of women, the topic of women safety cannot be ignored. For the same reason, we are developing this application to eliminate certain threats such as thieving, molestation again women, and so on. Thus, to provide safety to each and every citizen of our country.

What it does

Our project mainly focuses on the security for everyone in the city. The goal is to engage the citizen in exchanging information and alerts and provide for a platform to develop inclusive problem solving methodologies. With the front-end using Flutter, we will develop a cross friendly mobile application which will send SOS signal, on shaking his device if a person is in danger, an SMS or a call will be sent to the emergency contact number provided at the time of registering in the application. Apart from that, using Machine learning, we will be predicting the way for the user, such that if the route on which certain crime ratio is more, the person can be notified. We also have image processing, such that a person can record the crime and the sharp objects or the objects which are not safe, can be identified using image processing. Thus, a project with many domains and technologies, which will provide safety to the citizens of the city. Project also has a smart bot who keeps detailed information about user's location ,bus or train details etc. Bot smartly saves the records and send this information to emergency contact, if bot finds out that the predicted zone is dangerous.

How we built it

Our team members, separately worked on different modules such as Tracking of location, detecting sharp/harmful objects such as knife, gun using Image Processing, wherever the user will move, his location will be detected and suddenly some crime is about to happen, the user can shake his mobile and thus, that shake will send a message to user's emergency contact number, which the user will provide while registering in the app. Machine Learning is also done, such that while the user walks through the particular road, user can view certain amount of crimes happened on that particular area, also the crimes can be predicted by the dataset available. And zones are classified as low level danger zone, medium and high level zone, such that user can predict how much safe that area is.

Challenges we ran into

Getting the dataset was a biggest challenge and so was the predicting using Machine Learning.

What we learned

To make the app user friendly, we followed golden rules of Human Computer Interaction to make the app as a real time use.

What's next for City Cop

The project can be expanded for the cops, so that cops can analyze amount of crimes in a particular area. All that FIR launching can be made through our project, rather than all the manual work done in the cities.

Built With

• api
• dart
• firebase
• flutter
• image-processing
• machine-learning
• python