High Frame Rate Light-Weight Siamese Network Target Tracking

Abstract

With the widespread use of target tracking in many life scenarios, the demand for high-precision and high-speed tracking algorithms is also increasing. For some specific scenarios such as mobile terminals, embedded devices, etc., under the premise of relatively insufficient computing power of the device, it is still necessary to ensure that the tracker achieves good tracking accuracy and high-speed real-time tracking. A high frame rate tracking algorithm based on light-weight siamese network is proposed to solve this problem. Firstly, the light-weight convolutional neural network MobileNetV1 is selected, which is easy to be deployed in embedded devices, as the feature extraction backbone network, and deep network is more capable of extracting target features. Then, two optimization strategies are proposed to address the shortcomings of the backbone network, feature map is cropped and the total network step length is adjusted to make the backbone network suitable for tracking tasks. Finally, after the template branch of the siamese network, an ultra-lightweight channel attention module is added to weight important information that highlights the target characteristics. The proposed algorithm parameters are reduced by 59.8% in comparison with current mainstream algorithm SiamFC. Simulation and experimental results on the OTB2015 dataset show that the tracking accuracy is increased by 5.4%, and the algorithm can better cope with complex and changeable challenges in tracking tasks. Simulation and experimental results on the VOT2018 dataset show that the comprehensive index expected average overlap (EAO) is increased by 26.6%, and the average speed of the algorithm under NVIDIA GTX1080Ti is 120 frame/s, which achieves high frame rate real-time tracking.

Keywords

• |target tracking|mobilenet|siamese networks|channel attention mechanism