Anomaly 2 benchmark results4/10/2023 ![]() To obtain satisfactory performance, extracting effective and discriminative features is crucial for such anomaly detection methods. The performance of these methods is highly dependent on training samples. For these methods, anomaly scores of video sequences are predicted by extracting features such as Histogram of Optical Flow (HOF) or dynamic texture (DT) with a trained classifier . Although reconstruction-based anomaly detection methods are good at reconstructing normal patterns in video sequences, the key issue in these methods is that they rely heavily on training data.Īnother type of anomaly detection method regards anomaly detection as a classification problem . At the testing stage, these methods utilize the differences between abnormal and normal samples to determine the final anomaly score of testing data, such as the reconstruction cost or specific threshold . The goal of these methods is to learn a feature representation model for normal patterns. One type of anomaly detection method is designed by reconstruction and they focus on modelling normal patterns in video sequences . ![]() Traditionally, anomaly detection methods are designed from two aspects. In practice, it is difficult to build effective anomaly detection models due to the unknown event type and indistinct definition of anomaly. In the last few years, there have been many studies investigating anomaly detection in the research community .Ĭompared with normal behaviours, an event that rarely occurs or with low probability is generally considered as anomaly. As a high-level computer vision task, anomaly detection aims to effectively distinguish abnormal and normal activities as well as anomaly categories in video sequences. Īnomaly detection, which attempts to automatically predict abnormal/normal events in a given video sequence, has been actively studied in the field of computer vision. Supplementary materials are available at. Experimental results show that the proposed method outperforms the state-of-the-art anomaly detection methods on our database and other public databases of anomaly detection. With the global spatiotemporal contextual feature, the anomaly type and score can be computed simultaneously by a multi-task neural network. Then we construct a recurrent convolutional neural network fed the local spatiotemporal contextual feature to extract the spatiotemporal contextual feature. We firstly obtain the local spatiotemporal contextual feature by using an Inflated 3D convolutional (I3D) network. Leveraging the above benefits from the LAD database, we further formulate anomaly detection as a fully supervised learning problem and propose a multi-task deep neural network to solve it. 2) It provides the annotation data, including video-level labels (abnormal/normal video, anomaly type) and frame-level labels (abnormal/normal video frame) to facilitate anomaly detection. with large scene varieties, making it the largest anomaly analysis database to date. 1) It contains 2000 video sequences including normal and abnormal video clips with 14 anomaly categories including crash, fire, violence etc. To tackle these problems, we contribute a new Large-scale Anomaly Detection ( LAD) database as the benchmark for anomaly detection in video sequences, which is featured in two aspects. Secondly, training sets contain only video-level labels indicating the existence of an abnormal event during the full video while lacking annotations of precise time durations. However, existing anomaly detection databases encounter two major problems. IET Generation, Transmission & DistributionĪnomaly detection has attracted considerable search attention.IET Electrical Systems in Transportation.IET Cyber-Physical Systems: Theory & Applications.IET Collaborative Intelligent Manufacturing.CAAI Transactions on Intelligence Technology.
0 Comments
Leave a Reply.AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |