An improved mobile vehicle detection method based on Gaussian mixture model

作者：Lu, Qing-Hua^*; Wu, Zhi-Wei; Fan, Yan-Bin; Zhang, Xian-Min

来源：Journal of Optoelectronics - Laser, 2013, 24(4): 751-757.

• Background subtraction
• Computing speed
• Gaussian Mixture Model
• Intelligent traffics
• Mobile vehicle
• Moving vehicles
• Used vehicles
• Vehicle detection

To overcome the defects of the Gaussian mixture model (GMM) in widely used vehicle detection method, an improved algorithm for moving vehicle detection based on Gaussian mixture model (GMM) is proposed. For the defect of long-term "ghosts"in Gaussian mixture model, a new update method of weights and variances is employed to accelerate the elimination of "ghosts", and the performance of vehicle detection can be improved. Additionally, in the existing GMM, all of the pixels are modeled by fixed number of distributions, so main memories are wasted. To save memories, a self-adapting method is adopted. For the pixels whose distribution numbers are not up to maximum, the approach of adaptive change for distribution numbers is used to effectively decrease the total number of distributions and save memory space. Experimental results show that the improved GMM method provides superior performance in the elimination of "ghosts"and computing speed for vehicle detection.

• 出版日期2013-4
• 单位华南理工大学