This work is about recognizing human activities occurring in videos at distinct semantic levels, including individual actions, interactions, and group activities. The recognition is realized using a two-level hierarchy of Long Short-Term Memory (LSTM) networks, forming a feed-forward deep architecture, which can be trained end-to-end. In comparison with existing architectures of LSTMs, we make two key contributions giving the name to our approach as Confidence-Energy Recurrent Network -- CERN. First, instead of using the common softmax layer for prediction, we specify a novel energy layer (EL) for estimating the energy of our predictions. Second, rather than finding the common minimum-energy class assignment, which may be numerically unstable under uncertainty, we specify that the EL additionally computes the p-values of the solutions, and in this way estimates the most confident energy minimum. The evaluation on the Collective Activity and Volleyball datasets demonstrates: (i) advantages of our two contributions relative to the common softmax and energy-minimization formulations and (ii) a superior performance relative to the state-of-the-art approaches.

本文介绍了使用一种新颖的深度神经网络结构——CERN来识别人类视频中的个人动作、交互和群体活动，并相对其他现有的LSTM结构作出了两个重要的贡献，一个是提出一种新型的能量层（EL）来估计预测能量，另一个是使用最可信的能量最小值而非找到常见的最小能量类分配，并在测试集上得到了显著的性能提升。

CERN：用于群体活动识别的置信度-能量循环网络