Tracking pedestrians from a moving camera based on Kalman filter

Abstract

The target tracking and object tracking are defined in this paper and the difference between multi-target tracking and multi-object tracking is also be illustrated. The Bayes filter, Kalman filter, EKF, JPDA and Hungarian Algorithm are introduced with formulars and an example of moving camera to track the pedestrians used by Kalman filter are shown. In this example, the method which is based on Kalman filter that track pedestrians from a moving car which is installed with camera in the field of the multi-object tracking is analyzed with steps. The algorithm initializes boundary boxes to track the pedestrians and predict the pedestrians based on the previous position. Then, update the tracks and delete the useless tracks. The final step is creating the tracks. After displaying the result, the algorithm based on Kalman filter can successfully track the pedestrians with boundary boxes. However, when the camera is moving fast, some of the pedestrians cannot be recognized.