Object recognition, identification and classification for intelligent surveillance and reconnaissance platforms

Raymond Ptucha; Aneesh Bhat; Aravindh Kuppusamy; Sergey E. Lyshevski

doi:10.1117/12.2519300

7 May 2019 Object recognition, identification and classification for intelligent surveillance and reconnaissance platforms

Raymond Ptucha, Aneesh Bhat, Aravindh Kuppusamy, Sergey E. Lyshevski

Author Affiliations +

Proceedings Volume 11018, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVIII; 110180M (2019) https://doi.org/10.1117/12.2519300
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

Abstract

We research solutions to enable intelligence, surveillance and reconnaissance by means of near-real-time target recognition, identification and classification. The cloud services, intelligence, surveillance, target acquisition and reconnaissance are of importance for the C⁴⁺iSTAR systems. These platforms are expected to ensure high-level cognitive autonomy to accomplish complex missions and tasks in rapidly-changing adverse environments. We research and apply deep learning concepts and algorithms to enable high-confidence awareness and advance situational analysis. We examine engineering solutions for object identification and classification. Our findings ensure sufficient level of fidelity on the object recognition and classification likelihood with high identification probability, processing latency on low-power ARM CPUs, and, integration capabilities. Advanced concepts on moving target recognition and object classification using fly data are researched with low-fidelity experimental substantiations. We modify the YOLOv3 object detection method to detect bounding boxes for arbitrary orientation angle, angle of view and corner shapes which are of importance in aerial applications. Our results demonstrate adequate detection capability while maintaining fast computational performance of the original YOLOv3 architecture. The proposed algorithms and computing schemes are supported by codes in C++.

Conference Presentation

Citation Download Citation

Raymond Ptucha, Aneesh Bhat, Aravindh Kuppusamy, and Sergey E. Lyshevski "Object recognition, identification and classification for intelligent surveillance and reconnaissance platforms", Proc. SPIE 11018, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVIII, 110180M (7 May 2019); https://doi.org/10.1117/12.2519300

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