Target Adaptive Tracking Based on GOTURN Algorithm with Convolutional Neural Network and Data Fusion

• Published in: Computational intelligence and neuroscience Vol. 2021; no. 1; p. 4276860
• Main Authors: Li, Zhengze, Xu, Jiancheng
• Format: Journal Article
• Language: English
• Published: United States Hindawi 2021; John Wiley & Sons, Inc
• Subjects: Accuracy; Algorithms; Artificial Intelligence; Artificial neural networks; Data integration; Deep learning; Feature extraction; Feature maps; Humans; Methods; Neural networks; Neural Networks, Computer; Neurophysiology; Robustness (mathematics); Tracking
• ISSN: 1687-5265; 1687-5273; 1687-5273
• DOI: 10.1155/2021/4276860

With the advent of the artificial intelligence era, target adaptive tracking technology has been rapidly developed in the fields of human-computer interaction, intelligent monitoring, and autonomous driving. Aiming at the problem of low tracking accuracy and poor robustness of the current Generic Object Tracking Using Regression Network (GOTURN) tracking algorithm, this paper takes the most popular convolutional neural network in the current target-tracking field as the basic network structure and proposes an improved GOTURN target-tracking algorithm based on residual attention mechanism and fusion of spatiotemporal context information for data fusion. The algorithm transmits the target template, prediction area, and search area to the network at the same time to extract the general feature map and predicts the location of the tracking target in the current frame through the fully connected layer. At the same time, the residual attention mechanism network is added to the target template network structure to enhance the feature expression ability of the network and improve the overall performance of the algorithm. A large number of experiments conducted on the current mainstream target-tracking test data set show that the tracking algorithm we proposed has significantly improved the overall performance of the original tracking algorithm.