Segmentation-based low-resolution spatial tracker for video

Minas Benyamin; Geoffrey H. Goldman

doi:10.1117/12.3016503

7 June 2024 Segmentation-based low-resolution spatial tracker for video

Minas Benyamin, Geoffrey H. Goldman

Proceedings Volume 13039, Automatic Target Recognition XXXIV; 130390E (2024) https://doi.org/10.1117/12.3016503
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Abstract

Low-resolution image object recognition and tracking is often required for battlefield reconnaissance. For high-cost military systems, standard signal processing techniques can be used by tracking systems, however, low-cost systems require simpler approaches. We developed a fast detector-agnostic tracker for improving situational awareness using electro-optical video data. Our approach uses low computational techniques such as YOLO, match filters, and shape transforms to segment objects of interest in an image. From two or more successive detections, we initialize an alpha-beta filter that predicts the location of the target of interest in the image. Next, we segment subsequent frames to a search area around the predicted region. This increases the sensitivity of the detector by improving the average signal-to-noise ratio and it also decreases the false alarm rate. The reduction in the size of the processing area can improve the detection speed per frame by an order of magnitude relative to a full-sized frame. By using algorithms with input from variable-size object a such as YOLO, this algorithm can be adapted to track virtually any object captured in a video.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Minas Benyamin and Geoffrey H. Goldman "Segmentation-based low-resolution spatial tracker for video", Proc. SPIE 13039, Automatic Target Recognition XXXIV, 130390E (7 June 2024); https://doi.org/10.1117/12.3016503

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