A Predictor of Moving Objects for First-Person Vision
Ricardo Sánchez-Matilla, Andrea Cavallaro · 2019
Predicting the motion of objects captured by a moving camera is important for first-person vision tasks. In this paper, we present an accurate model to forecast the position of moving objects by disentangling global and object motion without the need of camera calibration or planarity assumptions. Our predictor uses past observations to model online the motion of objects by selectively tracking a spatially balanced set of keypoints and estimating scene transformations between pairs of frames. We show that we can forecast up to 60% more accurately than state-of-the-art predictors while being resilient to noisy observations. Moreover, the proposed predictor is robust to frame-rate reduction and outperforms alternative approaches while processing only 33% of the frames with moving cameras. We also show the benefit of integrating the proposed predictor in a multi-object tracker.