Motion Based Particle Filter for Human Tracking with Thermal Imaging
—Motion Tracking has been applied in many recent applications like surveillance, advance driver assistance system (ADAS), non-cooperative biometrics, virtual reality, etc. Current research in this field includes making tracking system more robust and reliable. Imaging modality to be used in motion tracking also includes thermal imaging (FIR) in addition to the visible imaging. We propose here a motion tracking algorithm using only thermal imaging. The particle filter uses motion based features pre-processed with Wigner distribution. The comparison between the performance measure obtained for thermal and visible imaging is also presented. The results obtained with the proposed system show the robustness of the algorithm. Additionally, the algorithm has less computational complexity as calculation of motion based features requires fewer computations. I. INTRODUCTION Recent advances in digital storage and video hardware's has lead to new applications like surveillance, advance driver assistance system, non-cooperative biometrics, virtual reality etc. The cheaper cost and easy availability of imaging modality has attracted researcher's attention in motion tracking system to be applied in number of applications. The main objective of motion tracking is to analyze the apparent motion in image sequences. Supplementary to visible imaging, recently, thermal imaging has been exploited in applications like motion tracking and face recognition. This has attracted attention from computer vision researchers towards this new imaging modality. There are some attempts to include thermal imaging along with visible camera through data fusion techniques. However, if only one of the modalities is used and reliable results are obtained with it, the system would benefit from the cost saving and higher processing speed.