Deep Hashing and Sparse Representation of Abnormal Events Detection

Due to its widespread application in the field of public security, anomaly detection in crowd scenes has recently become a hot topic. Some deep learning-based methods led to significant accomplishments in this field. Nevertheless, due to the scarcity of data and the misclassification of queries which most of them suffer to some extent from a sudden and infrequent overfitting. Though, we tried to solve the above problems, understand the long video streams and establish an accurate and reliable security system in order to improve its performance in detecting anomalies. We also referred to the hash technique, which has proven to be the most efficient method used when researching about large-scale image recovery. Thus, this article offers a smart video anomaly detection solution. In this paper, we combine the advantages of both deep hashing and deep auto-encoders to show that tracking changes in deep hash components across time and can be used to detect local anomalies. More precisely, we start with a new technique to minimize the mass of input data and information in order to decrease the time of calculation using a new dynamic frame skipping technique. Then, we propose to measure local anomalies by combining semantic with low-level optical flows to balance the performance and perceptibility. The experimental results illustrate that the proposed methods surpass these baselines for the detection and localization of anomalies.