Can I read Object Tracking Using Spatio-Temporal Future Prediction on EtoBox?

Object Tracking Using Spatio-Temporal Future Prediction by Liu, Yuan; Li, Ruoteng; Tan, Robby T.; Cheng, Yu; Sui, Xiubao is a scholarly article available to read on EtoBox.

What is Object Tracking Using Spatio-Temporal Future Prediction about?

Occlusion is a long-standing problem that causes many modern tracking methods to be erroneous. In this paper, we address the occlusion problem by exploiting the current and future possible locations of the target object from its past trajectory. To achieve this, we introduce a learning-based tracking method that takes into account background motion modeling and trajectory prediction. Our trajectory prediction module predicts the target object's locations in the current and future frames based on the object's past trajectory. Since, in the input video, the target object's trajectory is not only affected by the object motion but also the camera motion, our background motion module estimates the camera motion. So that the object's trajectory can be made independent from it. To dynamically switch between the appearance-based tracker and the trajectory prediction, we employ a network that can assess how good a tracking prediction is, and we use the assessment scores to choose between the appearance-based tracker's prediction and the trajectory-based prediction. Comprehensive evaluations show that the proposed method sets a new state-of-the-art performance on commonly used tracking bench

Author

Liu, Yuan; Li, Ruoteng; Tan, Robby T.; Cheng, Yu; Sui, Xiubao

Published

2020

Language

EN