摘要
Abstract
Siamese network tracking algorithms can transform tracking problems into similarity matching problems,but most algorithms can-not be implemented in engineering applications on mobile devices or embedded devices with insufficient computing power.To this end,a light-weight tracking algorithm based on siamese networks is proposed,selecting ShuffleNetV2 as the core network that can be used on mobile devic-es.Aiming at the shortcomings of the original network,four optimization operations are proposed:eliminating the influence of padding layer,modifying activation function,adopting upsampling,and modifying step size.At the same time,attention mechanism is introduced to further strengthen the connection between features.Simulation experiments were conducted on the OTB100 and UAV123 datasets,and the results showed that compared with existing tracking algorithms,the proposed algorithm has excellent comprehensive performance.At the same time,it has good robustness in the face of various complex factors such as deformation,low resolution,and scale transformation.关键词
目标跟踪/ShuffleNetV2/孪生网络/注意力机制Key words
target tracking/ShuffleNetV2/siamese network/attention mechanism分类
信息技术与安全科学
