In the last few years there is a huge advancement in image processing technology. It can be implemented in wide variety of applications, one of the applications can be applied in automatic seat identification in public transportation systems by detecting and processing digital pictures. Face detection and recognition is a branch of image processing that can be used to detect human face from specific region. In this paper, we are focusing on techniques based on real time human face identification and a webcam can be utilized to capture the digital image to count the number of passengers entering or leaving the public transport via gate and count the passenger gender for calculation of available seat on bus based on gender. For this purpose, the webcam can be deployed at the entrance of public vehicles and connected with Raspberry Pi processor module. This onboard webcam will take pictures of passengers entering or leaving the public vehicle as soon as the vehicle departs. The amount of noise present in the picture can be minimized through the software. 4G communications may be used to transmit data to server and after that the server will utilize face detection technology to process the digital images taken from the vehicles. This IoT-enabled framework then obtains the data of the number of persons entering or leaving the public transport with gender and subsequently processing of images resulting in calculation of the seat vacancy available in vehicle. This framework is efficient in terms of real-time data processing and transmission of data to remote locations regarding the number of passengers traveling at a particular time. In the last part of the paper, security concerns regarding the system will be discussed. The future scope of our framework will also be mentioned in the paper.
₹10000 (INR)
IEEE-2023