Current approaches for activity recognition often ignore constraints on computational resources: 1) they rely on extensive feature computation to obtain rich descriptors on all frames, and 2) they assume batch-mode access to the entire test video at once. We propose a new active approach to activity recognition that prioritizes "what to compute when" in order to make timely predictions. The main idea is to learn a policy that dynamically schedules the sequence of features to compute on selected frames of a given test video. In contrast to traditional static feature selection, our approach continually re-prioritizes computation based on the accumulated history of observations and accounts for the transience of those observations in ongoing video. We develop variants to handle both the batch and streaming settings. On two challenging datasets, our method provides significantly better accuracy than alternative techniques for a wide range of computational budgets.

本研究提出了一种新的活动识别方法，可以在不浪费计算资源的情况下及时预测，通过动态调度所选测试视频的帧上计算的特征序列来学习策略，相对于传统的静态特征选择，我们的方法可以持续优化计算，适用于批处理模式和流模式。在两个具有挑战性的数据集上，我们的方法在广泛的计算预算范围内提供了比替代技术更好的准确性。

流媒体视频中的动态特征优先级排序以进行活动检测