![]() ![]() Chong and Tay proposed an effective method for video anomaly detection, which is suitable for the spatiotemporal structure of video anomaly detection, including crowded scenes. The model will output the reconstructed sequence input at the current time and last time. proposed to combine Convolutional Long Short-Term Memory (Conv-LSTM) with Autoencoder to learn the appearance and action information of the video. proposed a cubic-patch-based way containing 3D deep autoencoders and 3D convolutional neural networks for an advanced feature-learning approach. proposed the Gaussian Mixture Variational Autoencoder, which used the Gaussian Mixture Model (GMM) to fit the distribution of the feature space through the variational method. Different from our method, it is to use a binary classifier to determine anomalies. first proposed to use unmasking to deal with learned characteristics. In contrast, we use the two-stream autoencoder network to learn features and generate the reconstructed video sequence to detect anomalies. SlowFast networks can be described as a single stream architecture that operates at two different frame rates. proposed to use SlowFast Networks for video recognition. proposed combining CNN high-level semantic information and low-level Optical-Flow as a new method of measuring anomalies.īesides, Feichtenhofer et al. Those methods train the model to get better detection results. ![]() Recently, more and more approaches have employed deep learning to learn the features of the video frame. The anomaly detection task mainly trains a regular model with only normal samples and then marks the samples in the test dataset different from the normal samples. ![]() Regarding practical use, to ensure the proposed method can produce reliable results, a model’s interpretability is required.Īdvances in computer vision have led to an interest in automated computational methods for video surveillance. Despite the excellent performance, none of these methods considers the black-box problem brought by deep learning models. Recently, another developing approach for video processing in the deep learning framework is two-stream networks, which have been successfully applied to video-based action recognition, often with state-of-the-art results. Although abnormal event detection has inspired plenty of works based on computer vision techniques, it is still quite challenging to design a general detection framework because of the definition uncertainty and limitations of the data-generating mechanism.ĭeep learning technologies have been widely used to detect abnormal events, including unsupervised methods and weakly supervised methods. As video cameras continue to expand, exploiting video data is currently severely limited by the amount of human effort, so that the automatic detection of rare or unusual incidents and activities in a surveillance video is urgently needed. It is an essential task in the computer vision field, from both academia and industry. The video’s abnormal event detection is to find events different from usual, such as people fighting or urgent events like fire. Experimental results on Avenue, UCSD Ped2, and Subway datasets show that our method can detect abnormal events well and explain the internal logic of the model at the object level. Furthermore, we present post hoc interpretability of feature map visualization to show the process of feature learning, revealing uncertain and ambiguous decision boundaries in the video sequence. Here, we introduce a two-stream approach that offers an autoencoder-based structure for fast and efficient detection to facilitate anomaly detection from surveillance video without labeled abnormal events. ![]() However, fast and reliable detection of abnormal events is still a challenging work. The growing interest in deep learning approaches to video surveillance raises concerns about the accuracy and efficiency of neural networks. ![]()
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