Moving Object Tracking Based on Histograms of Oriented Grads and Colors
Chang Xiang-kui · Journal of Henan University · 2007
An improved moving object tracking algorithm is presented based on the framework of traditional Kalman filter and Mean-Shift optimization. Firstly, by the fusion of Oriented Grads and Colors histogram a new histograms of Oriented Grads Colors (HoGC) is proposed. Secondly, HoGC pyramid is constructed to more robustly characterize the multi-scale objects. Finally, by coupling kernel-based Mean-Shift algorithm with Kalman Filter, HoGC matching is optimized in terms of scale and displacement of the candidate object identified. Experiments demonstrate that the proposed HoGC is robust to tracking moving objects and is invariant to scale and deformation. The proposed tracking algorithm can improve the reliability and accuracy without losing the real-time performance.