Change detection (CD) network for process-oriented model design improves detection efficiency through more complete time modeling. However, the networks accumulated by convolutional operations are limited by the localization of convolutional kernels, resulting in limited perception of spatiotemporal relationships. Therefore, in this letter, a process-oriented CD network based on discrete wavelet transform is proposed by combining the frequency-domain information in the convolutional network. Specifically, the network constructs a dual-time image into a multiframe video stream through video modeling and extracts the change features of different scales, frequencies, and directions in video and image features from the frequency-domain perspective with the help of discrete wavelet transform, which enhances the perception of spatiotemporal relationships. Experimental results on the LEVIR-CD, GVLM-CD, and EGY-BCD datasets validate the effectiveness of the network.

In recent years, many video anomaly detection methods have mainly used reconstruction and prediction based methods. However, due to the powerful encoding and decoding capabilities of autoencoders in reconstruction methods, the misjudgment rate of abnormal samples is high, and prediction methods are easily affected by environmental changes and data noise. Real time and accurate detection of pedestrian abnormal events still faces huge challenges. This article proposes a fast method VAD-KEMM for detecting abnormal behavior, which uses keyframe extraction and a multi task hybrid model. Firstly, key frames are extracted through segmented clustering and inter frame differences to improve detection efficiency; Then, a dual branch hybrid model is constructed using human skeletal information to improve reconstruction accuracy, and multi task learning is used to enhance prediction ability. The experimental results show that the AUC values of this method on the HR Shanghai Tech and HR Avenue datasets are 76.8% and 87.1%, respectively, indicating high detection efficiency and accuracy.

Classical unsupervised video anomaly detection methods learn normal patterns from normal video frames and assume significant reconstruction deviations for unforeseen anomalous video frames. These methods often introduce additional constraints to distinguish anomalies, potentially leading to higher reconstruction errors for normal instances. While specific methods have alleviated this issue by estimating multiscale deformation fields, these rely on additional background information that is susceptible to changes in the real world. In this paper, we propose a spatio-temporal relationship-based video anomaly detection STA-VAD method. By emphasizing spatio-temporal relationships in videos, our model better captures contextual information, reducing the impact of irrelevant background information. Additionally, we enhance the multiscale deformation field by adaptively adjusting it to quantify the severity of anomalies in video frames, thereby improving discrimination between normal and anomalous samples. In the widely recognized video anomaly detection datasets UCSD Ped2 and CUHK Avenue, our method demonstrates significant enhancements in accuracy and reliability, achieving state-of-the-art performance overall.

Anomaly detection in surveillance videos aims at identifying abnormal event under specific scenarios and it is widely applied in public security, smart city, and pedestrian surveillance. In the weakly-supervised setting, most existing anomaly detection approaches are formulated as the classic multiple-instance learning problem. In this paper, we provide a unique perspective that selection biased positive and unlabeled learning. In such a viewpoint, as long as estimating the label frequency from training set, we can effectively apply supervised classifier tow eakly supervised anomaly detection, and take greater advantage of these well-developed classifiers. For this purpose, we present a novel method to estimate label frequency from the attribute subdomains with large label probability. In the test phase, we only use the label frequency to modify the supervised classifier. Comprehensive experiments are performed on different scales datasets. Our method provides superior on all dataset which demonstrate the effectiveness.

Anomaly detection plays a critical role in intelligent video surveillance. However, real-world video data obtained always contains large numbers of normal video data, along with large numbers of unlabeled data. A promising solution with one-class classification and semi-supervised learning may not be satisfactory as they fail to make good use of unlabeled data with only normal data available. In this paper, we introduced a new framework, called Positive Unlabeled learning-based Anomaly event Detection (PU-AD), to exploit the weakly-supervised information. To the best of our knowledge, this is the first work that introduces the PU idea and achieves detecting abnormal events with a limited number of partially labeled data. Experiments on real-world surveillance videos show that the proposed method outperforms the existing state-of-the-art methods.