Browsing by Author "Ates, H. F."

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    Dual camera based high spatio-temporal resolution video generation for wide area surveillance
    (IEEE, 2022) Suluhan, Hasan Umut; Ates, H. F.; Gunturk, B. K.; Suluhan, Hasan Umut
    Wide area surveillance (WAS) requires high spatiotemporal resolution (HSTR) video for better precision. As an alternative to expensive WAS systems, low-cost hybrid imaging systems can be used. This paper presents the usage of multiple video feeds for the generation of HSTR video as an extension of reference based super resolution (RefSR). One feed captures video at high spatial resolution with low frame rate (HSLF) while the other captures low spatial resolution and high frame rate (LSHF) video simultaneously for the same scene. The main purpose is to create an HSTR video from the fusion of HSLF and LSHF videos. In this paper we propose an end-to-end trainable deep network that performs optical flow (OF) estimation and frame reconstruction by combining inputs from both video feeds. The proposed architecture provides significant improvement over existing video frame interpolation and RefSR techniques in terms of PSNR and SSIM metrics and can be deployed on drones with dual cameras.
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    Hybrid CPU-GPU acceleration of a multithreaded image stitching algorithm
    (IEEE, 2022) Tesfay, Shewıt Weldu; Demirdağ, Zeynep Gülbeyaz; Uğurdağ, Hasan Fatih; Ates, H. F.; Electrical & Electronics Engineering; UĞURDAĞ, Hasan Fatih; Tesfay, Shewıt Weldu; Demirdağ, Zeynep Gülbeyaz
    Real-time image stitching is critical, especially in un-manned aerial vehicles, and its acceleration has received attention in recent years. This paper describes an image stitching acceleration scheme for heterogeneous (CPU+GPU) devices. Acceleration is attempted with both multithreading and multiprocessing. Most time-critical functions in the algorithm are offloaded on to the GPU. We crafted a 3-buffer ping-pong mechanism for synchro-nization and data transfer among threads/processes in order to maximize CPU utilization. We carried out our experiments on Nvidia Jetson AGX Xavier. Results show that more than 3x acceleration is achieved.