Abstract
Nowadays, there is a rapid growth in the number of video cameras at public and private sector because of the monitoring and security purposes. As video surveillance using Closed Circuit Television (CCTV) is in boom nowadays, it has got more research attention due to increased global security concerns. This rapidly growing data can be used to automatically detect the anomalous activities which are going around in our surrounding. Anomalous activity is something that deviates from its normal nature or something that opposes the normal events. This research mainly focuses on detecting anomalous activities in crowded scenes by using video data. Automatically detecting the anomalous activity without using the handcrafted feature has become the need of the hour. This paper contains a survey of different approaches used for anomaly detection in the past. Different incremental and transfer learning approaches are discussed in this paper and it was found that incremental learning has not been used for video-based anomalous activity detection.
Highlights
A rapid growth in the number of data generated by the videos placed at indoor and outdoor places for the monitoring and security purpose has been observed
In the past few years, anomaly detection has become an area of interest for the research due to which various approaches has been proposed for detecting anomalies in crowded as well as non-crowded scenes
Incremental learning is a machine learning approach in which models learns from the new examples as they arrive without forgetting the knowledge previously learned
Summary
A rapid growth in the number of data generated by the videos placed at indoor and outdoor places for the monitoring and security purpose has been observed. First condition is that it should satisfy frame-level anomaly detection and second condition is that, if β percent of pixels are detected as an anomaly the frame is considered as an anomaly. This β parameter is defined by the user. This research aims to use incremental learning and transfer learning concepts along with convolutional neural network (CNNs) to detect anomalous activities in videos in crowded and non-crowded scenes. This survey is divided into three sections.
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