Multi-modal Semantic Data Fusion for Context-Aware Dynamic Object Tracking
Dynamic object tracking in surveillance and monitoring scenarios is one of the hottest topics in both research and development. Dynamic object tracking in complex environments is very challenging in accurately following objects while maintaining context awareness and respecting privacy. This project addresses this problem by exploring multi-modal semantic fusion techniques for enhancing context-aware dynamic object tracking. The primary aim is to develop an innovative approach leveraging diverse data sources to improve tracking accuracy and relevance while ensuring privacy preservation. The proposed research aligns with the demand for more accurate and contextually aware dynamic object tracking techniques while maintaining data privacy. By leveraging multi-modal data fusion, this research can contribute to advancing surveillance, robotics, and intelligent systems that require accurate tracking and contextual understanding. The outcomes could shape the development of future tracking technologies that incorporate diverse data sources to enhance tracking performance.
The research objectives are as follows: 1) investigate the integration of multi-modal data sources, including visual and non-visual data, for enhancing dynamic object tracking in various scenarios. 2) Develop fusion techniques that effectively combine semantic information from multiple modalities to guide tracking decisions and adapt to changing contexts. 3) Design privacy-preserving mechanisms allowing data sharing across modalities without revealing sensitive information. 4) Evaluate the proposed approach’s performance through extensive experimentation using diverse datasets and real-world tracking scenarios.