Anomaly Detection in Video Using Predictive Convolutional Long Short-Term Memory Networks
Anomaly Detection in Video Using Predictive Convolutional Long Short-Term Memory Networks
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发表时间：
2016-12
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通讯作者：
J. Medel;A. Savakis
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作者：
J. Medel;A. Savakis
自动检测长视频序列中的异常事件是具有挑战性的，由于这些事件是如何定义的模糊性。我们通过学习生成模型来解决这个问题，这些模型可以在有限的监督下识别视频中的异常。我们提出了端到端可训练的复合卷积长短期记忆（Conv-LSTM）网络，该网络能够从少量输入帧预测视频序列的演变。规则性得分是从具有异常视频序列的一组预测的重构误差中导出的，这些异常视频序列随着时间的推移进一步偏离实际序列，从而产生较低的规则性得分。该模型利用复合结构，并检查条件反射在学习更有意义的表示的影响。基于重建和预测精度选择最佳模型。对Conv-LSTM模型进行了定性和定量评估，展示了异常检测数据集上的竞争结果。Conv-LSTM单元被证明是建模和预测视频序列的有效工具。
Automating the detection of anomalous events within long video sequences is challenging due to the ambiguity of how such events are defined. We approach the problem by learning generative models that can identify anomalies in videos using limited supervision. We propose end-to-end trainable composite Convolutional Long Short-Term Memory (Conv-LSTM) networks that are able to predict the evolution of a video sequence from a small number of input frames. Regularity scores are derived from the reconstruction errors of a set of predictions with abnormal video sequences yielding lower regularity scores as they diverge further from the actual sequence over time. The models utilize a composite structure and examine the effects of conditioning in learning more meaningful representations. The best model is chosen based on the reconstruction and prediction accuracy. The Conv-LSTM models are evaluated both qualitatively and quantitatively, demonstrating competitive results on anomaly detection datasets. Conv-LSTM units are shown to be an effective tool for modeling and predicting video sequences.