Kwan, C., Chou, B. and Kwan, L. M. (2018) A Comparative Study of Conventional and Deep Learning Target Tracking Algorithms for Low Quality Videos. In: Huang, T., Lv, J., Sun, C. and Tuzikov, A., Eds., Advances in Neural Networks. Lecture Notes in Computer Science, Springer, Cham, 521-531.
https://doi.org/10.1007/978-3-319-92537-0_60

has been cited by the following article:

• TITLE: Deep Learning Based Target Tracking and Classification for Infrared Videos Using Compressive Measurements

  AUTHORS: Chiman Kwan, Bryan Chou, Jonathan Yang, Trac Tran

  KEYWORDS: Target Tracking, Classification, Compressive Sensing, SWIR, MWIR, LWIR, YOLO, ResNet, Infrared Videos

  JOURNAL NAME: Journal of Signal and Information Processing, Vol.10 No.4, November 29, 2019

  ABSTRACT: Although compressive measurements save data storage and bandwidth usage, they are difficult to be used directly for target tracking and classification without pixel reconstruction. This is because the Gaussian random matrix destroys the target location information in the original video frames. This paper summarizes our research effort on target tracking and classification directly in the compressive measurement domain. We focus on one particular type of compressive measurement using pixel subsampling. That is, original pixels in video frames are randomly subsampled. Even in such a special compressive sensing setting, conventional trackers do not work in a satisfactory manner. We propose a deep learning approach that integrates YOLO (You Only Look Once) and ResNet (residual network) for multiple target tracking and classification. YOLO is used for multiple target tracking and ResNet is for target classification. Extensive experiments using short wave infrared (SWIR), mid-wave infrared (MWIR), and long-wave infrared (LWIR) videos demonstrated the efficacy of the proposed approach even though the training data are very scarce.