摘要
An adaptive object tracking algorithm based on particle filtering and a modified Gradient Vector Flow (GVF) Snake is proposed for tracking moving and deforming objects. The original contours of objects are obtained by using the background differencing method,and the true contours of objects can be converged by means of the powerful searching ability of a modified GVF-Snake. Finally,an Energetic Particle Filtering (EPF) algorithm is obtained by combining particle filtering and a modified GVF-Snake,and by using K-means and the EPF algorithm,multiple objects can be tracked. The proposed tracking tactic for partially occluded objects can effectively improve its anti-occlusion ability. Experiments show that this algorithm can obtain better tracking effect even though the tracked object is occluded.
An adaptive object tracking algorithm based on particle filtering and a modified Gradient Vector Flow （GVF） Snake is proposed for tracking moving and deforming objects. The original contours of objects are obtained by using the background differencing method, and the true contours of objects can be converged by means of the powerful searching ability of a modified GVF-Snake. Finally, an Energetic Particle Filtering （EPF） algorithm is obtained by combining particle filtering and a modified GVF-Snake, and by using K-means and the EPF algorithm, multiple objects can be tracked. The proposed tracking tactic for partially occluded objects can effectively improve its anti-occlusion ability. Experiments show that this algorithm can obtain better tracking effect even though the tracked object is occluded.
基金
Supported by the Significant Term of Science and Technology Research in Ministry of Education (No. 205060)
Open Research Fund of National Mobile Communications Research Laboratory,Southeast University (N200911)
Significant Basic Research of Jiangsu Province Colleges and Universities Natural Science Projects (07 KJA51006)
Research Fund of Nanjing College of Traffic Vocational Technology (JY0903)
Huawei Science and Technology Fund
