Dual Temporal Differencing for Real-Time Abandoned Object Detection

Abstract

In recent years, ensuring safety in the public domain has posed significant challenges, prompting extensive research into developing intelligent surveillance cameras capable of detecting suspicious items before emergencies arise. Also, it is imperative to mitigate the loss of life and property resulting from terrorist attacks within the public sphere, necessitating effective safety measures. We propose Dual Temporal Buffer Differencing (DTBD) and a Single Short Deep Convolutional Neural Network (SSDCNT) object detector to address these challenges. We built two buffers to model the background independently and uniquely segment the static foreground region. The approach can detect the candidate stationary object using contour methodology with the single shot deep convolutional neural network object detector. The candidate object is validated and identified as abandoned and attended. Our approach is unique because it is robust to various illumination changes without affecting the foreground element, and it can detect suspicious objects in any crowd or complex scenario. Its application will mitigate the risk of a potential terrorist attack in the public domain by sensing and alerting the security expert before a situation becomes an emergency. The performance of our proposed approach, the benefits, and the implementation challenges were evaluated through a publicly available dataset ABODA and PEET 2006. The result demonstrates that our proposed approach outperformed other state-of-the-art algorithms in detecting suspicious items. As a result, the approach can facilitate safety in the public domain. It will enable the security personnel to respond proactively to the suspicious item and de-escalate it before the situation becomes an emergency.