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Automatic detection, localization and interpretation of an unusual event in a sequence of video is a challenging task due to its equivocal and complex nature. The development of deep neural networks have paved the way for more efficient recognition and analysis of anomalous events in video data. With the introduction of convolutional neural network (CNN) and Long short-term memory (LSTM), the spatial and temporal features extraction became easier. In this paper, we propose an end-to-end trainable Inter-fused Autoencoder (IFA) which is designed using the assemblage of CNN and LSTM layers to detect the unwonted events in a video sequence. The proposed architecture is capable of exploiting both the spatial and temporal variation of video data. The reconstruction error is computed in terms of both MSE and PSNR for each testing video. A comparison is also carried out between MSE and PSNR to show that which assessment technique is better for a reconstructive model for recreating the video sequence. A well-optimized threshold is calculated which decides the fate of testing event i.e. either usual or unusual event. Using benchmark datasets, multiple experiments were carried out to demonstrate the efficacy of proposed architecture. [ABSTRACT FROM AUTHOR]

Due to the increase in surveillance systems, there is a massive increase in surveillance data. As of now, the key challenge for video surveillance systems is analyzing these large video clips. It, therefore, has an enormous demand for intelligent video analysis systems capable of identifying activities and events. Since many researchers have emphasized the role of contextual knowledge and how the performance of video content analysis has improved in several ways, we have looked at different approaches in this study that can extract semantic information to human-level perception in the video. We also addressed open problems in semantics that come from event detection and irregular activity detection. Most methods/models are too coarse to accurately extract a complete set of information. Thus, we need to use a machine-readable format to view, process, store and extract meaningful information from the video data. In this paper, we discussed the methods/approaches for extracting low-level features, mid-level features, and high-level video features and their representation using Semantic Technologies. A taxonomy of hierarchical feature generation approaches is also provided. Some evaluation metrics for evaluating video activity and measuring the performance of the extraction features are explored. Community-approved benchmark datasets are also thoroughly surveyed and presented. The paper provides a complete framework of video research to develop an intelligent surveillance system. [ABSTRACT FROM AUTHOR]