Target tracking and classification using compressive sensing camera for SWIR videos
| Autor: | Trac D. Tran, Chiman Kwan, Jonathan Yang, Jack Zhang, Bryan Chou, Ralph Etienne-Cummings, Akshay Rangamani |
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| Rok vydání: | 2019 |
| Předmět: |
Pixel
business.industry Computer science Deep learning Frame (networking) ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 020206 networking & telecommunications 02 engineering and technology Lossy compression Tracking (particle physics) Residual Compressed sensing Signal Processing Compression ratio 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer vision Artificial intelligence Electrical and Electronic Engineering business |
| Zdroj: | Signal, Image and Video Processing. 13:1629-1637 |
| ISSN: | 1863-1711 1863-1703 |
| DOI: | 10.1007/s11760-019-01506-4 |
| Popis: | The pixel-wise code exposure (PCE) camera is a compressive sensing camera that has several advantages, such as low power consumption and high compression ratio. Moreover, one notable advantage is the capability to control individual pixel exposure time. Conventional approaches of using PCE cameras involve a time-consuming and lossy process to reconstruct the original frames and then use those frames for target tracking and classification. Otherwise, conventional approaches will fail if compressive measurements are used. In this paper, we present a deep learning approach that directly performs target tracking and classification in the compressive measurement domain without any frame reconstruction. Our approach has two parts: tracking and classification. The tracking has been done via detection using You Only Look Once (YOLO), and the classification is achieved using residual network (ResNet). Extensive simulations using short-wave infrared (SWIR) videos demonstrated the efficacy of our proposed approach. |
| Databáze: | OpenAIRE |
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