Volume 10, Number 6

Deep Learning Based Target Tracking and Classification Directly in Compressive
Measurement for Low Quality Videos

  Authors

Chiman Kwan¹, Bryan Chou¹, Jonathan Yang² and Trac Tran³, ¹Applied Research LLC, USA, ²Google, Inc., USA and ³Johns Hopkins University, USA

  Abstract

Past research has found that compressive measurements save data storage and bandwidth usage. However, it is also observed that compressive measurements 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 type of compressive measurement using pixel subsampling. That is, the compressive measurements are obtained by randomly subsample the original pixels in video frames. Even in such special setting, conventional trackers still do not work well. We propose a deep learning approach that integrates YOLO (You Only Look Once) and ResNet (residual network) for target tracking and classification in low quality videos. YOLO is for multiple target detection and ResNet is for target classification. Extensive experiments using optical and mid-wave infrared (MWIR) videos in the SENSIAC database demonstrated the efficacy of the proposed approach.

  Keywords

Compressive measurements, target tracking, target classification, deep learning, YOLO, ResNet, optical videos, infrared videos, SENSIAC database