  In this paper, we construct a lightweight, high-precision and high-speed
object tracking using a trained CNN. Conventional methods with trained CNNs use
VGG16 network which requires powerful computational resources. Therefore, there
is a problem that it is difficult to apply in low computation resources
environments. To solve this problem, we use MobileNetV3, which is a CNN for
mobile terminals.Based on Feature Map Selection Tracking, we propose a new
architecture that extracts effective features of MobileNet for object tracking.
The architecture requires no online learning but only offline learning. In
addition, by using features of objects other than tracking target, the features
of tracking target are extracted more efficiently. We measure the tracking
accuracy with Visual Tracker Benchmark and confirm that the proposed method can
perform high-precision and high-speed calculation even in low computation
resource environments.
