GDJN: Global density joined network for abnormal event detection in surveillance videos

Abstract

Detecting abnormal events in surveillance involves identifying unexpected behavior through video analysis. This involves recognizing patterns or deviations from normal behavior and taking actions to mitigate potential risks. However, the distribution of data can change over time, leading to concept drift, which can make it challenging to accurately detect abnormal events. To address this issue, a new approach using a global density network (GDN) has been proposed. The GDN allows for more efficient identification of object distributions in surveillance videos, leading to improved accuracy in abnormal event detection. The proposed method combines features extracted by a backbone network with a global density joined network (GDJN), which refines density features using dilated convolutional networks. A multistage long short-term memory (LSTM) network is then used to classify abnormal events. The experimental results are conducted on two datasets, UMN and UCSD Ped2. The achieved F1 scores were 93.42 and 94.46 respectively, with corresponding AUC values of 93.5 and 94.8.