Kalman filter and iterative-hungarian algorithm implementation for low complexity point tracking as part of fast multiple object tracking system
2016 International Conference on Frontiers of Information Technology (FIT) (2016)
Dec. 19, 2016 to Dec. 21, 2016
Bima Sahbani , Electrical Engineering Department, ITB, Jalan Ganesa No 10 Bandung, Indonesia
Widyawardana Adiprawita , Biomedical Engineering Department, ITB, Jalan Ganesa No 10 Bandung, Indonesia
Multiple object tracking is one field of study in vision-based navigation. Basically, in many current-research of this study, the common method in object tracking consists two main parts such as object detection and motion prediction, Object detection is highly related to the image processing which has a high tendency in using biggest part of computation resources. But, for the certain purpose, the computation resources should be used in proper way. In this paper, the problem will be limited in the case of football player tracking which needs so many computation resources for the image processing such the motion prediction algorithm that should be significantly reduced this need without decrease the system performance. In this paper, there will be explained the implementation of modified Hungarian algorithm and Kalman Filter as the main core of the motion prediction system in this case. Kalman Filter is the simplest algorithm for motion prediction, but in the case of multiple objects, there should be an additional computing to identify detected object before it will be tracked. Besides that, the system should have an ability to maintain object's identity in many worst case like occlusion, and boundary condition. In this paper, there also be explained the performance and consideration to increase system performance. In the highlight, the implementation result state that multiple point tracking module successfully being implemented as a part of fast multiple object tracking systems for football. The main indicator is identity maintainability which reaches 10% in 800 sample frames. Besides that, the computation parameter could be quantified using statistics that include the threshold value for data association is 2 meters per frame and 6 meters per frame for occlusion threshold. In the computational resources needs analysis, using worst case approximation, the system will need approximately 26 KB memory for computing in each frame. By this result, the conclusion is that the tracking system is proper to be implemented in football player tracking system both from the computation resources need and the performance.
Multiple point tracking, Multiple Object Tracking, Kalman Filter, Hungarian-Iterative Algorithm
B. Sahbani and W. Adiprawita, "Kalman filter and iterative-hungarian algorithm implementation for low complexity point tracking as part of fast multiple object tracking system," 2016 International Conference on Frontiers of Information Technology (FIT), Islamabad, Pakistan, 2016, pp. 109-115.