Object Tracking Algorithm Based on Adaptive Deep Sparse Neural Network

Abstract

Due to the complexity of object tracking easy to produce the tracking drift problem, this paper proposes an object tracking algorithm based on deep sparse neural network. In the particle filter framework, using the Rectifier Linear Unit (ReLU) activation function, according to different situations of object to construct a deep sparse neural network structure, through the finite sample label on-line training, this algorithm can get a robust tracking network. The experimental results show that compared with the current mainstream tracking algorithm, the average tracking success rate and accuracy of algorithm are greatly improved, and according to changes in light, occlusion and fast object movement in complex environment, the algorithm can effectively solve the problem of tracking drift, and show good robustness.