Object Tracking by Combining Tracking-by-Detection and Marginal Particle Filter

In this paper, we propose a new algorithm based on tracking-by-detection approach for tracking of the objects having non-linear dynamic motion. For this purpose, the tracking-by-detection method depending on the Gauss kernel using the circulant matrix theory and the Fourier transform is employed together with the marginal particle filter method. Marginal particle filter uses the scores derived from the Gauss kernel at the measurement update phase of the filter to weight the particles propagated around the target that has been tracked. While updating the state variables by marginal particle filter, the object coordinates, the correction values belonging to these coordinates and the dimensions of the image window surrounding the object is estimated. The proposed method is tested on the video sequences which include the object having a high non-linear motion, and it was observed that marginal particle filter enhanced the performance of the track-by-detection method in an important scale.