An Optimized CNN based Real World Anomaly Detection in Surveillance Videos

Abstract

Anomaly detection in automated surveillance video is an extremely monotonous process for monitoring for crowded scenes and surveillance videos are capable to incarcerate a mixture of sensible anomalies. An appropriate machine learning technique can help to train the Anomaly Detection System (ADS) in identifying anomalous activities during surveillance. To this end, we present an anomaly detection system that can be used as a tool for anomaly detection in surveillance videos using the concept of artificial intelligence. The main intention of the proposed anomaly detection system is to improve the detection time and accuracy by using the concept of Convolutional Neural Network (CNN) as artificial intelligence technique. In this paper we present a CNN based Anomaly Detection System (CNN-ADS), which is the combination of multiple layer of hidden unit with the optimized MSER feature by using Genetic Algorithm (GA). Here CNN is used for classifying the activity into normal and abnormal from the surveillance videos based on the fitness function of GA which is used for the selection of optimal MSER feature sets. Further, Self adaptive genetic algorithm (SAGA) is adopted to efficiently solve optimization problems in the continuous search domain to select the best possible feature to segregate the pattern of normal and abnormal activities. The main contribution of this research is validation of proposed system for the large scale data and we introduce a new large-scale dataset of 128 hours of videos. Dataset consists of 1900 long and untrimmed real-world surveillance videos, with 13 sensible anomalies such as road accident, burglary, fighting, robbery, etc. as well as normal activities. The experimental results of the planned system show that our CNN-ADS for anomaly detection achieve essential improvement on anomaly detection presentation as compared to the state-of-the-art approaches. The dataset is available at: https://webpages.uncc.edu/cchen62/dataset.html. In this paper, to validate the proposed ADS we provide the comparison of existing results of several recent deep learning baselines on anomalous activity detection. The real-time ADS in surveillance video sequences using SAGA based CNN with MSER feature extraction technique is implemented using Image Processing Toolbox within Matlab Software.