计算机科学
自编码
异常检测
人工智能
Boosting(机器学习)
光流
编码器
渲染(计算机图形)
模式识别(心理学)
火车
编码(集合论)
人工神经网络
机器学习
计算机视觉
图像(数学)
地图学
集合(抽象数据类型)
程序设计语言
地理
操作系统
作者
Shifeng Li,Cheng Yan,Liang Zhang,Xi Luo,Ruixuan Zhang
标识
DOI:10.1016/j.imavis.2024.105011
摘要
In this paper, we propose a comprehensive framework for detecting anomalies in videos based on autoencoder (AE). Traditional AE models solely rely on input and final reconstruction, potentially limiting their capacity to fully utilize the intermediate neural network layers. To mitigate this limitation, we introduce a novel approach that concurrently trains the model using corresponding intermediate layers from both the encoder and decoder. This allows the model to capture more intricate features, thus enhancing its anomaly detection capabilities. Furthermore, we introduce a motion loss function that exclusively relies on original video frames rather than optical flow, rendering it more efficient and capable of extracting motion features. Additionally, we have devised a variance attention strategy that is parameter-free and can automatically directs our model's focus towards moving objects, further boosting the performance of our approach. Our experiments on three public datasets demonstrate the effectiveness and efficiency of our method in identifying abnormal events in complex scenarios. The code is publicly available at https://github.com/lsf2008/multRecLossAEPub.
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