Safety within cabs is a major concern these days. Many cases prove that even the big cab providing companies like Ola and Uber is not at all a completely safe option. Research shows that in previous five years there have been many cases in the past that ruined many trips for various people who chose to travel in cabs like Uber and Ola, instead of other modes of transportation.
Applications involving SOS signals are launched in the market regularly, but most of them fail to work in the time of real emergency, the only reason being that at time of emergency, an individual does not get enough time to actually trigger that SOS signal.
The best solution to this problem is to completely automate the task completed by prior detection of the problem and even in case of problems, removing the human part completely.
We addressed the problems like accidents due to the carelessness of driver, quarrels or fights between driver and passenger, and came up with a product which will solve all these along with ensuring women safety
The initial stage was to detect drowsiness of a person, which took time for setup because of the constraints like for how much time the eyes should be open to classifying the person as drowsy. The yawn detection part has been a challenge too because of false positives by the bottom of the face. We trained models a few times to get better results each time. Violence detection was a problem faced too. There are several cases where it would fail to detect violence in a frame. With the higher amount of steps, we have increased the accuracy of its detection. The final part was to make a profanity detection model, but we failed to do it because of less time. Its training takes 4hrs on a 1080ti GPU, and so with limited hardware, we couldn't implement it in time. We are open to adding it in future.