![]() The results confirm that the proposed model is robust and significantly outperforms state-of-the-art methods in terms of accuracy. In order to evaluate the performance of the proposed model, experiments are performed on one of the most challenging real-world anomalies UCF-Crime dataset. Secondly, a new deep learning model based on the Inflated Inception network (I3D) is proposed for learning spatial and temporal information from video frames. Firstly, a novel strategy of dynamic frame-skipping is proposed for producing meaningful temporal sequences for model learning. In this research work, our contribution is two-fold. There is a need to re-investigate the problem from the frame sequences perspective to better define an activity in the limited temporal length. Deep learning architectures have a limited input temporal sequence length and suffer from learning very long sequences. Some events are as fast to be captured within a few frames however, some activities are slow and may require several thousands of video frames to define an activity. The time period required for an anomalous activity to be completely understandable and meaningful depends on the nature and speed of the event. A key factor in defining activity is the temporal length or duration of the activity. Real-world anomalous events are far more complex and harder to capture due to diverse human behaviors and a wide range of anomaly types. Therefore, we provide researchers with recommendations and guidelines based on this review.Īnomaly detection has significant importance for developing autonomous surveillance systems. Detection of anomalies using ML models is a promising area of research, and there are a lot of ML models that have been implemented by researchers. In addition, we observe that unsupervised anomaly detection has been adopted by researchers more than other classification anomaly detection systems. ![]() Finally, we present 22 different datasets that are applied in experiments on anomaly detection, as well as many other general datasets. Moreover, we identify 29 distinct ML models used in the identification of anomalies. After analyzing the selected research articles, we present 43 different applications of anomaly detection found in the selected research articles. In our review, we have identified 290 research articles, written from 2000-2020, that discuss ML techniques for anomaly detection. Our review analyzes the models from four perspectives the applications of anomaly detection, ML techniques, performance metrics for ML models, and the classification of anomaly detection. In this research paper, we conduct a Systematic Literature Review (SLR) which analyzes ML models that detect anomalies in their application. One of the increasingly significant techniques is Machine Learning (ML), which plays an important role in this area. Many techniques have been used to detect anomalies. Īnomaly detection has been used for decades to identify and extract anomalous components from data. Code related to this paper is available at:, ,. The empirical results demonstrate that our approach is effective and robust in detecting group anomalies. We conduct extensive experiments to evaluate our models on real world datasets. Both AAE and VAE detect group anomalies using point-wise input data where group memberships are known a priori. In this paper, we take a generative approach by proposing deep generative models: Adversarial autoencoder (AAE) and variational autoencoder (VAE) for group anomaly detection. ![]() GAD is an important task in detecting unusual and anomalous phenomena in real-world applications such as high energy particle physics, social media and medical imaging. In particular, we perform group anomaly detection (GAD) with an emphasis on irregular group distributions (e.g. Unlike conventional anomaly detection research that focuses on point anomalies, our goal is to detect anomalous collections of individual data points.
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