Forecasting Hand and Object Locations in Future Frames

This paper presents an approach to forecast future locations of human hands and objects. Given an image frame, the goal is to predict presence and location of hands and objects in the future frame (e.g., 5 seconds later), even when they are not visible in the current frame. The key idea is that (1) an intermediate representation of a convolutional object recognition model abstracts scene information in its frame and that (2) we can predict (i.e., regress) such representations corresponding to the future frames based on that of the current frame. We design a new two-stream convolutional neural network (CNN) architecture for videos by extending the state-of-the-art convolutional object detection network, and present a new fully convolutional regression network for predicting future scene representations. Our experiments confirm that combining the regressed future representation with our detection network allows reliable estimation of future hands and objects in videos.