Moving object detection method based on complementary multi resolution background models
来源期刊：中南大学学报(英文版)2014年第6期
论文作者：屠礼芬 ZHONG Si-dong(仲思东) PENG Qi(彭祺)
文章页码：2306 - 2314
Key words：moving object detection; complementary Gaussian mixture models; intermittent object motion; thermal and dynamic background
Abstract: A novel moving object detection method was proposed in order to adapt the difficulties caused by intermittent object motion, thermal and dynamic background sequences. Two groups of complementary Gaussian mixture models were used. The ghost and real static object could be classified by comparing the similarity of the edge images further. In each group, the multi resolution Gaussian mixture models were used and dual thresholds were applied in every resolution in order to get a complete object mask without much noise. The computational color model was also used to depress illustration variations and light shadows. The proposed method was verified by the public test sequences provided by the IEEE Change Detection Workshop and compared with three state-of-the-art methods. Experimental results demonstrate that the proposed method is better than others for all of the evaluation parameters in intermittent object motion sequences. Four and two in the seven evaluation parameters are better than the others in thermal and dynamic background sequences, respectively. The proposed method shows a relatively good performance, especially for the intermittent object motion sequences.
TU Li-fen(屠礼芬), ZHONG Si-dong(仲思东), PENG Qi(彭祺)
(School of Electronic Information, Wuhan University, Wuhan 430072, China)
Abstract:A novel moving object detection method was proposed in order to adapt the difficulties caused by intermittent object motion, thermal and dynamic background sequences. Two groups of complementary Gaussian mixture models were used. The ghost and real static object could be classified by comparing the similarity of the edge images further. In each group, the multi resolution Gaussian mixture models were used and dual thresholds were applied in every resolution in order to get a complete object mask without much noise. The computational color model was also used to depress illustration variations and light shadows. The proposed method was verified by the public test sequences provided by the IEEE Change Detection Workshop and compared with three state-of-the-art methods. Experimental results demonstrate that the proposed method is better than others for all of the evaluation parameters in intermittent object motion sequences. Four and two in the seven evaluation parameters are better than the others in thermal and dynamic background sequences, respectively. The proposed method shows a relatively good performance, especially for the intermittent object motion sequences.
Key words:moving object detection; complementary Gaussian mixture models; intermittent object motion; thermal and dynamic background
