Real-Time Detection Of Suspicious Roadside Objects In Surveillance Imagery

ABSTRACT Real-time detection of suspicious roadside objects is an important task for improving public safety and preventing potential threats in urban environments. This system focuses on automatically identifying unusual or potentially dangerous objects such as unattended bags, boxes, or debris from surveillance camera footage. By using advanced computer vision and deep learning techniques, the model analyzes live video streams and detects objects placed near roadsides in real time. The detected objects are classified and monitored to determine whether they pose a potential risk. When a suspicious object is identified, the system can generate alerts to assist security personnel and authorities in taking timely action. This approach helps enhance surveillance efficiency, reduces manual monitoring efforts, and supports faster response to possible security threats. The rapid growth of urban environments and transportation networks has increased the need for intelligent surveillance systems to ensure public safety. This project presents a system for real-time detection of suspicious roadside objects in surveillance imagery using advanced computer vision and deep learning techniques. The proposed system analyzes live video streams from roadside surveillance cameras to automatically identify unusual or potentially dangerous objects such as abandoned bags, unattended packages, or hazardous materials. A deep learning–based object detection model is employed to accurately detect and classify objects in real time, while additional contextual analysis helps determine whether the object is suspicious based on its location, duration of presence, and surrounding activity. The system continuously monitors the scene and generates alerts to authorities when a suspicious object is detected, enabling faster response and preventive action. By integrating automated detection with surveillance infrastructure, the proposed approach enhances situational awareness, reduces the need for constant human monitoring, and improves the overall efficiency of public safety systems.