Analyzing human actions in videos has gained increased attention recently. While most works focus on classifying and labeling observed video frames or anticipating the very recent future, making long-term predictions over more than just a few seconds is a task with many practical applications that has not yet been addressed. In this paper, we propose two methods to predict a considerably large amount of future actions and their durations. Both, a CNN and an RNN are trained to learn future video labels based on previously seen content. We show that our methods generate accurate predictions of the future even for long videos with a huge amount of different actions and can even deal with noisy or erroneous input information. Predicting future frames, poses or image segmentations in videos has been studied in several works [20, 16, 13, 31, 11, 27, 18, 29, 33, 32, 17, 12] . Approaches that predict future frames at a pixel-level, however, are limited to a few frames. Instead of predicting frames, a deep network is trained in [30] to predict visual representations of future frames. The predicted visual representations can then be 5343
More recently, Farha et al. REF proposed a deep network to predict future activity.
When will you do what? - Anticipating Temporal Occurrences of Activities
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Computer Science