Despite a lot of research efforts devoted in recent years, how to efficiently learn long-term dependencies from sequences still remains a pretty challenging task. As one of the key models for sequence learning, recurrent neural network (RNN) and its variants such as long short term memory (LSTM) and gated recurrent unit (GRU) are still not powerful enough in practice. One possible reason is that they have only feedforward connections, which is different from the biological neural system that is typically composed of both feedforward and feedback connections. To address this problem, this paper proposes a biologically-inspired deep network, called shuttleNet. Technologically, the shuttleNet consists of several processors, each of which is a GRU while associated with multiple groups of hidden states. Unlike traditional RNNs, all processors inside shuttleNet are loop connected to mimic the brain's feedforward and feedback connections, in which they are shared across multiple pathways in the loop connection. Attention mechanism is then employed to select the best information flow pathway. Extensive experiments conducted on two benchmark datasets (i.e UCF101 and HMDB51) show that we can beat state-of-the-art methods by simply embedding shuttleNet into a CNN-RNN framework.

Human action recognition is widely recognized as a challenging task due to the difficulty of effectively characterizing human action in a complex scene. Recent studies have shown that the dense-trajectory-based methods can achieve state-of-the-art recognition results on some challenging datasets. However, in these methods, each dense trajectory is often represented as a vector of coordinates, consequently losing the structural relationship between different trajectories. To address the problem, this paper proposes a novel Deep Trajectory Descriptor (DTD) for action recognition. First, we extract dense trajectories from multiple consecutive frames and then project them onto a canvas. This will result in a “trajectory texture” image which can effectively characterize the relative motion in these frames. Based on these trajectory texture images, a deep neural network (DNN) is utilized to learn a more compact and powerful representation of dense trajectories. In the action recognition system, the DTD descriptor, together with other non-trajectory features such as HOG, HOF and MBH, can provide an effective way to characterize human action from various aspects. Experimental results show that our system can statistically outperform several state-of-the-art approaches, with an average accuracy of 95:6% on KTH and an accuracy of 92.14% on UCF50.