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An Augmented Treble Stream Deep Neural Network for Video Analysis

机译：用于视频分析的增强高音流深神经网络

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Video analysis for human action recognition is one of the most important research areas in pattern recognition and computer vision due to its wide applications. Deep learning-based approaches have been proven more effective than conventional feature engineering-based models. However, the performance is still unreliable when facing real-world application scenarios. Inspired by the Convolutional Neural Network (CNN) and Recurrent Long-Short Term Model (LSTM), this paper presents an augmented treble-stream deep neural network architecture that supports direct extraction of spatial-temporal features from video streams and their corresponding dense optical flows. This innovative approach assists effective detection of complex video event features that are annotated by rich event "appearance" and motion features. Substantially improved recognition accuracy is recorded during the experiments that are carried and benchmarked over public video event datasets, for example, UCF 101 and HMDB 51. Analytical evaluation approves the validity and effectiveness of the treble-stream neural network design.
机译：人类行动识别的视频分析是由于其广泛的应用而导致的模式识别和计算机视觉中最重要的研究领域之一。已经证明了基于深度学习的方法比以往的基于特征工程的模型更有效。但是，面对现实世界应用方案时，性能仍然不可靠。由卷积神经网络（CNN）和复发性长短期模型（LSTM），本文提出的增强高音流深的神经网络结构的启发，支持从视频流的空间 - 时间特征和它们的对应的致密的光流直接提取。这种创新方法有助于有效地检测由丰富的事件“外观”和运动功能注释的复杂视频事件功能。在经过公共视频事件数据集的实验期间记录基本上改善的识别准确度，例如，UCF 101和HMDB 51。分析评估批准了高音流神经网络设计的有效性和有效性。

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