Open AccessA lightweight convolutional network for infrared object detection and tracking
Author(s) -
Yin Xu,
Qiang Fan
Publication year - 2022
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2234/1/012004
Subject(s) - artificial intelligence , computer vision , computer science , tracking (education) , feature (linguistics) , inference , infrared , object detection , eye tracking , video tracking , convolutional neural network , object (grammar) , pattern recognition (psychology) , optics , psychology , pedagogy , linguistics , philosophy , physics
As an important application of computer vision, visual tracking has being a fundamental topic. Compared with visible image, infrared image has the characteristic of low resolution, blurred contour and single color feature. Thus, it is still a challenge for infrared object tracking. Further, it is difficult to balance the real-time performance and accuracy. This paper proposed a method for target detection and tracking, with a deeper and lightweight MobileNet V2 structure as the backbone network. In the end, the tracker is tested on various datasets. Result shows that the tracker can get a balance between tracking accuracy and inference speed, which is crucial for deployment on mobile devices.