SensePred: Guiding Video Prediction by Wearable Sensors

Jia-Yan Li, Jaden Chao-Ho Lin, Kun-Ru Wu, Yu-Chee Tseng

Abstract: Video prediction has been studied in several earlier works. However, there are two inherent limitations in existing solutions, one being the ambiguity concern and the other being the degradation problem in lengthy predictions. To overcome these limitations, this work studies using wearable inertial sensors to guide video prediction. We create a data set called Pedestrians with IMU (Ped-IMU) that records people walking around with the wearable devices to collect the relation between inertial measurement unit (IMU) and video data and propose a SensePred model to conquer these limitations. The model takes full wearable sensor information and partial video information as inputs and predicts the missing video information. Simulation results validate the effectiveness of our model.