Practical Abandoned Object Detection in Real-World Scenarios: Enhancements Using Background Matting With Dense ASPP

Abstract

The widespread deployment of Closed-Circuit Television (CCTV) systems in public and private spaces has significantly enhanced security measures but also posed unique challenges in accurately interpreting the voluminous data captured, especially in the context of abandoned object detection. This area is critical for identifying potential security threats, including illegal waste disposal, explosives, or lost items, which necessitate sophisticated detection techniques. Traditional methods often struggle with limitations such as false positives/negatives due to dynamic environmental conditions like lighting changes or complex backgrounds. Addressing these challenges, our study proposes a novel abandoned object detection system that integrates background matting and advanced learning algorithms to refine detection accuracy. The system architecture is divided into three key stages: i) preprocessing, to reduce noise and adjust for lighting variations; ii) abandoned object recognition (AOR), employing background matting to distinguish between static and dynamic entities, further enhanced by pedestrian detection to exclude moving objects; and iii) abandoned object decision feature correction (AODFC), which employs feature correlation analysis for precise identification of abandoned objects. The experimental evaluation, conducted across varied real-world settings, demonstrates the method's superior performance over conventional approaches, significantly reducing false identifications while maintaining high detection accuracy. This paper not only presents a comprehensive solution to the challenges of abandoned object detection but also paves the way for future research in enhancing the robustness and applicability of surveillance systems.