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首页> 外文会议>Computer Graphics International Conference >'Forget' the Forget Gate: Estimating Anomalies in Videos Using Self-contained Long Short-Term Memory Networks
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'Forget' the Forget Gate: Estimating Anomalies in Videos Using Self-contained Long Short-Term Memory Networks

机译：“忘记”忘记门：使用自包含的长短期内存网络估算视频中的异常

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摘要

Abnormal event detection is a challenging task that requires effectively handling intricate features of appearance and motion. In this paper, we present an approach of detecting anomalies in videos by learning a novel LSTM based self-contained network on normal dense optical flow. Due to their sigmoid implementations, standard LSTM's forget gate is susceptible to overlooking and dismissing relevant content in long sequence tasks. The forget gate mitigates participation of previous hidden state for computation of cell state prioritizing current input. Besides, the hyperbolic tangent activation of standard LSTMs sacrifices performance when a network gets deeper. To tackle these two limitations, we introduce a bi-gated, light LSTM cell by discarding the forget gate and introducing sigmoid activation. Specifically, the proposed LSTM architecture fully sustains content from previous hidden state thereby enabling the trained model to be robust and make context-independent decision during evaluation. Removing the forget gate results in a simplified and undemanding LSTM cell with improved performance and computational efficiency. Empirical evaluations show that the proposed bi-gated LSTM based network outperforms various LSTM based models for abnormality detection and generalization tasks on CUHK Avenue and UCSD datasets.
机译：异常事件检测是一个具有挑战性的任务，需要有效地处理外观和运动的复杂特征。在本文中，我们通过在正常致密光学流上学习一种新的LSTM的自包含网络来介绍视频中的异常检测异常。由于它们的统计实施，标准LSTM的忘记门易于忽略和解雇长期任务中的相关内容。忘记门可以减轻先前隐藏状态的参与，以计算电池状态优先输入电流输入。此外，当网络变得更深时，标准LSTMS的双曲线切线激活牺牲性能。为了解决这两个限制，我们通过丢弃忘记门并引入乙状结肠激活来引入双门控光的LSTM单元。具体地，所提出的LSTM架构完全维持了先前隐藏状态的内容，从而使训练的模型能够在评估期间具有稳健性并使上下文无关的决定。删除忘记门会导致简化和未定的LSTM单元，具有改进的性能和计算效率。实证评估表明，所提出的双门式基于LSTM的网络优于基于SSTM的基于LSTM的模型，用于CUHK Avenue和UCSD数据集的异常检测和泛化任务。

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