A Competitive Neural Network for Multiple Object Tracking in Video Sequence Analysis

作者：Luque Baena Rafael M^*; Ortiz de Lazcano Lobato Juan M; Lopez Rubio Ezequiel; Dominguez Enrique; Palomo Esteban J

来源：Neural Processing Letters, 2013, 37(1): 47-67.

DOI：10.1007/s11063-012-9268-3

• Growing competitive neural networks
• Feature weigthing
• Feature selection
• Genetic Algorithms
• Multiple object tracking

Tracking of moving objects in real situation is a challenging research issue, due to dynamic changes in objects or background appearance, illumination, shape and occlusions. In this paper, we deal with these difficulties by incorporating an adaptive feature weighting mechanism to the proposed growing competitive neural network for multiple objects tracking. The neural network takes advantage of the most relevant object features (information provided by the proposed adaptive feature weighting mechanism) in order to estimate the trajectories of the moving objects. The feature selection mechanism is based on a genetic algorithm, and the tracking algorithm is based on a growing competitive neural network where each unit is associated to each object in the scene. The proposed methods (object tracking and feature selection mechanism) are applied to detect the trajectories of moving vehicles in roads. Experimental results show the performance of the proposed system compared to the standard Kalman filter.

• 出版日期2013-2