The development of object recognition application to automatically count the number of passengers entering the bus Abzal Adilzhan, Serikbolsyn Myrzakhmet Department of Electrical and Electronic Engineering, Nazarbayev University, Astana, Kazakhstan In Kazakhstan, bus operating companies traditionally hire bus conductors for manual fare collection from the passengers. However, the control of correspondence of the produced revenue with the issued tickets by the conductor is a critical problem. As a result of controller injustice the bus companies might receive less revenue. Also, it might result in inaccurate bus timetable and route management. Therefore, the passenger flow count is a crucial concern for bus companies. Introduction Objective This project aims to create an object recognition application which will automatically count the number of passengers entering the bus. The application was developed by using C++ program and OpenCV library. The process of project implementation can be divided into several steps:  Video Capture  Background Subtraction  Reducing Noise and Separating Objects  Removing Shadows  Defining Object Contours  Determining Mass Center  Counting Detected Objects Methods Results Conclusions According to the results it can be concluded that the application is able to capture video, implement background subtraction, reduce noise and separate objects, remove shadows, define the object contours and mass center, and it count s only entering passengers. The limitations of the project are the sensitivity of the method on light conditions, some possible errors when two passengers pass the region very close, and that it might be efficient only for busses with single operating door. For the future similar projects it is recommended to develop a count method which can differentiate and count people based on human head parameters. A window represented in Figure 1 shows the video frame with the foreground mask of the object. As it can be seen from this image the frame contains noise, disturbances and shadows. Figure 2 illustrates the foreground mask with removed shadow, reduced noise and eliminated holes. This frame was used to calculate the object’s contour and center mass. Figure 3 shows the original frame with rectangle shapes around the moving objects for observation, green colored centroid, red colored regions of interest for counting, and displayed number of entered passengers. Figure 4 represents the case when the object centroid is inside the range of interest. According to this picture it can be referred that when the counter was activated, the region modified its color to green. However, a problem occurs when the intensity of the light significantly varies since the background subtraction technique is sensitive to light changes. This problem can be solved by detecting objects in a static condition and adjusting application to both daytime and nighttime bus light conditions [4]. Moreover, since the method of this project is based on object detection, in addition to people count this application computes objects with significant size. Yet, this issue was ignored due to the fact that large objects must also be provided with tickets. 1.Bradski, Gary and Adrian Kaebler, Learning OpenCV. United States of America: O’Reilly Media, Laganiere, Robert, OpenCV2 Computer Vision Application Programming Cookbook. Birmingham: Packt Publishing Ltd, Mukherjee, Satarupa, Stephani Gil and Nilanjan Ray, “Unique people count from monocular videos”, The Visual Computer, pp1-13, Sep Putra, Samuel Mahatma, Renan Prasta Jenie, I Made Vidyasthana, Harry Yanto, and Agustinus Wijiaya, “Designing object detection prototype for people counter using computer vision,” unpublished. 5.Wang, Hanzi, and David Suter, "A re-evaluation of mixture of Gaussian background modeling [video signal processing applications]." In Acoustics, Speech, and Signal Processing, Proceedings.(ICASSP'05). IEEE International Conference on, vol. 2, pp. ii IEEE, References Figure 1. The screenshot of the foreground mask window. Figure 2. The screenshot of the foreground mask with removed shadow window. Figure 3. The screenshot of the original frame. Figure 4. The screenshot of the original frame.