Temporal Transformer Networks With Self-Supervision for Action Recognition

Abstract

In recent years, Internet of Things (IoT) has made rapid development, and IoT devices are developing towards intelligence. IoT terminal devices represented by surveillance cameras play an irreplaceable role in modern society, most of them are integrated with video action recognition and other intelligent functions. However, their performance is somewhat affected by the limitation of computing resources of IoT terminal devices and the lack of long-range non-linear temporal relation modeling and reverse motion information modeling. To address this urgent problem, we introduce a startling Temporal Transformer Network with Self-supervision (TTSN). Our high-performance TTSN mainly consists of a temporal transformer module and a temporal sequence self-supervision module. Concisely speaking, we utilize the efficient temporal transformer module to model the non-linear temporal dependencies among non-local frames, which significantly enhances complex motion feature representations. The temporal sequence self-supervision module we employ unprecedentedly adopts the streamlined strategy of “random batch random channel” to reverse the sequence of video frames, allowing robust extractions of motion information representation from inversed temporal dimensions and improving the generalization capability of the model. Extensive experiments on three widely used datasets (HMDB51, UCF101, and Something-something V1) have conclusively demonstrated that our proposed TTSN is promising as it successfully achieves state-of-the-art performance for video action recognition. Our TTSN provides the possibility for its application in IoT scenarios due to its computational complexity and high performance. With the rapid development of the Internet of Things (IoT), more and more data is being disseminated in the form of video, which also puts new requirements on the understanding and modeling of video data. In recent years, 2D Convolutional Networks-based video action recognition has encouragingly gained wide popularity; However, constrained by the lack of long-range non-linear temporal relation modeling and reverse motion information modeling, the performance of existing models is, therefore, undercut seriously. To address this urgent problem, we introduce a startling Temporal Transformer Network with Self-supervision (TTSN). Our high-performance TTSN mainly consists of a temporal transformer module and a temporal sequence self-supervision module. Concisely speaking, we utilize the efficient temporal transformer module to model the non-linear temporal dependencies among non-local frames, which significantly enhances complex motion feature representations. The temporal sequence self-supervision module we employ unprecedentedly adopts the streamlined strategy of “random batch random channel” to reverse the sequence of video frames, allowing robust extractions of motion information representation from inversed temporal dimensions and improving the generalization capability of the model. Extensive experiments on three widely used datasets (HMDB51, UCF101, and Something-something V1) have conclusively demonstrated that our proposed TTSN is promising as it successfully achieves state-of-the-art performance for video action recognition. As a result, our work provides new attention and self-supervised algorithm for processing video data in IoT.