Real-time video-shot detection for scene surveillance applications

Area of Science:

• Computer Vision
• Artificial Intelligence
• Security Systems

Background:

• Traditional surveillance systems often require manual review of footage, which is time-consuming and prone to errors.
• Detecting abandoned objects and understanding the context of security events are critical for effective threat assessment.
• Automated video analysis can significantly enhance the efficiency and accuracy of security operations.

Purpose of the Study:

• To develop an automated surveillance system capable of detecting abandoned objects and performing online semantic video segmentation.
• To facilitate the task of human operators in identifying the cause of security alarms.
• To automatically segment video content and identify key frames related to suspicious activities.

Main Methods:

• Image segmentation using temporal rank-order filtering for abandoned object detection.
• Classification algorithms to reduce false alarms in object detection.
• Temporal video segmentation for analyzing alarm events.
• Key frame extraction based on movement features around detected objects.

Main Results:

• The system demonstrates capabilities in static region detection and classification.
• Performance evaluation includes clip and key-frame detection errors across varying environmental complexities.
• Experimental results provide insights into system performance in different real-world scenarios.

Conclusions:

• The proposed system offers automated detection of abandoned objects and semantic video segmentation.
• It effectively aids human operators in rapidly determining the cause of security alarms.
• The system's performance is validated through experimental results, indicating its potential for enhanced security surveillance.