A poisson process model for activity forecasting

Abstract

Activity forecasting has recently become an active research area for its importance in critical applications like automated navigation and human-computer interaction. However, for a video observed upto a certain time, all of the existing forecasting works focus on predicting the activity label, i.e., predicting what the next unobserved activity is. To the best of our knowledge, no work has answered the crucial question yet: when the next unobserved activity will occur. In this paper, we propose an approach for predicting the starting time of the next unobserved activity without assuming that we know its label. We model activities occurring at a variable rate using a Log-Gaussian Cox Process (LGCP) and learn the rate function from the training data. Then the starting time is predicted using importance sampling algorithm. In our experiments on the challenging MPII-Cooking dataset, we find that both the label of the last observed activity and the label of the activity being predicted affect the time prediction accuracy.