This paper proposes a robust dual-model matching and model update algorithm for object tracking with camera in motion. With the proposed technique, a short-term model is matched then updated each frame when it is matched to reflect the most recent changes of the tracked object in illumination, size and deformation. Furthermore, a long-term model is maintained in a period of time then updated in a lower frequency to against the interference of rapid and temporal change of the above factors. In addition, a statistic-based sub-region update strategy rather than global update for both short-term and long-term tracking models is utilized. The proposed algorithm is intensity-based, but it can be extended to any feature matching, such as color, texture and shape appearance. An embedded version of this algorithm has been implemented with Texas Instruments' DM6446-based DSP system. Extensive experiments and practical applications in different situations confirm the robustness and reliability of the proposed method.
|Title of host publication||3rd International Conference on Imaging for Crime Detection and Prevention, ICDP 2009|
|Publication status||Published - 1 Dec 2009|
|Event||3rd International Conference on Imaging for Crime Detection and Prevention, ICDP 2009 - London, United Kingdom|
Duration: 3 Dec 2009 → 3 Dec 2009
|Conference||3rd International Conference on Imaging for Crime Detection and Prevention, ICDP 2009|
|Period||3/12/09 → 3/12/09|
- Illumination change