Real time pedestrian tracking using adaptive kalman filter

Abstract

Image processing has been one of the hot topics since early 1950's and it has developed from simple researches on the images to real time video processing. This development brought more challenges to the researches as the environment around became a complex variable to process. Thus new techniques with low computing time and flexible algorithms that adapt to changes in the environment emerged. Human detection and estimation of their movements is one of the research areas described before. For security and emergency situations, applications of real time human detection is fundamental. In this work it was aimed to develop a robust system that can predict human movement on real time video. The motivation behind the work is to design a system that can detect pedestrians and their movement to warn the driver beforehand. The system needed to do the lowest amount of computation time as possible and it needed to adapt to changes of the environment in order to work in real time with unstable surroundings. Thus an Adaptive Kalman algorithm is developed for prediction of the next steps as only the previous steps information was needed. For the development of the project Matlab is used as the programming language. The reason behind choosing Matlab is due to the fact that Matlab IDE includes many libraries and toolboxes useful for this work. As the first step of the project the camera input is evaluated in real time by taking snapshots and further algorithms are applied on the frames taken. For human detection the built in Matlab library which gets data from the Caltech Dataset is used. The data gathered from the human detection algorithm is used to find the centroids in the region of interest. Centroids are fed to the Adaptive Kalman Filter as the input data with corresponding error parameters. The term Adaptive Kalman Filter comes from the responsive error tuning from the previous input. P, Q and R parameters are tuned with the error values of the previous frame. It was observed that all P, Q and R parameters and K value is converged with the increasing number of frames. Thus the learning process is also included in the work. In the 1000 test frames the system is tested with single and multiple pedestrians in the frame. Out of total 1678 samples 1418 of them are the right classifications which results in a %84.5 success rate. In the test process, it was observed that the error is mainly caused when two objects move towards each other. From that observation it can be concluded that the system works better in areas where the crowd is less dense.