A cloud infrastructure for target detection and tracking using audio and video fusion

This paper presents a Cloud-based architecture for detecting and tracking multiple moving targets from airborne videos combined with the audio assistance, which is called Cloud-based Audio-Video (CAV) fusion. The CAV system innovation is a method for user-based voice-to-text color feature descriptor track matching with an automated hue feature extraction from image pixels. The introduced CAV approach is general purpose for detecting and tracking different valuable targets´ movement for suspicious behavior recognition through multi-intelligence data fusion. Using Cloud computing leads to real-time performance as compared a single machine workflow. The obtained multiple moving target tracking results from airborne videos demonstrate that the CAV approach provides improved frame rate, enhanced detection, and real-time tracking and classification performance under realistic conditions.