Embedded Implementation of a Real-Time Motion Estimation Method in Video Sequences

Bakó, László; Hajdu, Szabolcs; Brassai, Sándor Tihamér; Morgan, Frank; Enăchescu, Călin

doi:10.1016/j.protcy.2016.01.066

Cited by 4 publications

(4 citation statements)

References 5 publications

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“…Bako et al [16] presented an embedded implementation of a hardware-efficient method for motion information extraction from video signal, using an FPGA circuit-based system for on-line Sobel edge detection and edge displacementbased optical flow computation. The total execution time on a Xilinx Spartan-6 FPGA was approximately 1.6ms.…”

Section: Related Workmentioning

confidence: 99%

“…Another study by Jagannathan et al [14] ran a 7-layer CNN on TDA3x SoC for object classification, and the overall system performance was 15fps. The majority of existing work (Ratnayake et al [15], Bako et al [16], Nikitakis et al [17], Chen et al [18], Arva et al [19]) used only a single embedded device to conduct video processing, which is obviously not capable of fulfilling real-time video processing (24fps). Gao et al [20] built an embedded cluster for video processing, but it lacked a complete solution to address system stability and fault tolerance.…”

Section: Introductionmentioning

confidence: 99%

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A Streaming Cloud Platform for Real-Time Video Processing on Embedded Devices

Zhang

Zhao

et al. 2021

IEEE Trans. Cloud Comput.

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Real-time intelligent video processing on embedded devices with low power consumption can be useful for applications like drone surveillance, smart cars, and more. However, the limited resources of embedded devices is a challenging issue for effective embedded computing. Most of the existing work on this topic focuses on single device based solutions, without the use of cloud computing mechanisms for parallel processing to boost performance. In this paper, we propose a cloud platform for real-time video processing based on embedded devices. Eight NVIDIA Jetson TX1 and three Jetson TX2 GPUs are used to construct a streaming embedded cloud platform (SECP), on which Apache Storm is deployed as the cloud computing environment for deep learning algorithms (Convolutional Neural Networks-CNNs) to process video streams. Additionally, self-managing services are designed to ensure that this platform can run smoothly and stably, in the form of a metric sensor, a bottleneck detector and a scheduler. This platform is evaluated in terms of processing speed, power consumption, and network throughput by running various deep learning algorithms for object detection. The results show the proposed platform can run deep learning algorithms on embedded devices while meeting the high scalability and fault tolerance required for real-time video processing.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Streaming Cloud Platform for Real-Time Video Processing on Embedded Devices

Zhang

Zhao

et al. 2021

IEEE Trans. Cloud Comput.

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“…Optical flow algorithms are also time consuming but are sensitive to code transformations and can be highly accelerated [18]- [22]. We thus decided to use an optical flow algorithm in our real-time denoising chain.…”

Section: Denoising Algorithmmentioning

confidence: 99%

A New Real-Time Embedded Video Denoising Algorithm

Petreto

Romera

Lemaître

et al. 2019

2019 Conference on Design and Architectures for Signal and Image Processing (DASIP)

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Many embedded applications rely on video processing or on video visualization. Noisy video is thus a major issue for such applications. However, video denoising requires a lot of computational effort and most of the state-of-the-art algorithms cannot be run in real-time at camera framerate. This article introduces a new real-time video denoising algorithm for embedded platforms called RTE-VD. We first compare its denoising capabilities with other online and offline algorithms. We show that RTE-VD can achieve real-time performance (25 frames per second) for qHD video (960×540 pixels) on embedded CPUs and the output image quality is comparable to state-of-theart algorithms. In order to reach real-time denoising, we applied several high-level transforms and optimizations (SIMDization, multi-core parallelization, operator fusion and pipelining). We study the relation between computation time and power consumption on several embedded CPUs and show that it is possible to determine different frequency and core configurations in order to minimize either the computation time or the energy.

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“…CMLA's IPol website [4] also provides various codes of recent algorithms. Optimized implementations of optical flow algorithms were the subject of numerous works on FPGA [5], [6], [7], [8] and on GPU [9], [10], [11], [12], but few on CPU [11], [13]. It should also be noted that optical flow estimations based on machine learning are gaining in popularity in the scientific community [14], [15].…”

Section: Optical Flow Iterative Algorithmsmentioning

confidence: 99%

Energy and Execution Time Comparison of Optical Flow Algorithms on SIMD and GPU Architectures

Petreto¹,

Hennequin²,

Kœhler³

et al. 2018

2018 Conference on Design and Architectures for Signal and Image Processing (DASIP)

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This article presents and compares optimized implementations of two optical flow algorithms on several target boards comprising multi-core SIMD processors and GPUs. The two algorithms are Horn-Schunck (HS) and TV-L1, and have been chosen because they are both well-known, and because of their different computational complexity and accuracy. For both algorithms, we have made parallel optimized SIMD implementations, while HS has also been implemented on GPUs. For each algorithm, the comparison between the different versions and target boards is carried out in a two-dimensional fashion: in terms of computing speed-in order to achieve real-time computation-and in terms of energy consumption since we target embedded systems. The results show that for HS, the GPUs are the most efficient in both dimensions, able to process in realtime performances (25 frames per second) up to 8Mpix images for 0.35J per image, against 1.8Mpix images for 0.24J per image on CPU. The results also highlight the impact of optimizations on TV-L1: far slower than HS without optimization, it can almost match its performance after optimization on CPU, and can achieve real-time performances with 0.25J for 1.4Mpix images. We hope these results will help developers design optical flow embedded systems.

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Embedded Implementation of a Real-Time Motion Estimation Method in Video Sequences

Cited by 4 publications

References 5 publications

A Streaming Cloud Platform for Real-Time Video Processing on Embedded Devices

A Streaming Cloud Platform for Real-Time Video Processing on Embedded Devices

A New Real-Time Embedded Video Denoising Algorithm

Energy and Execution Time Comparison of Optical Flow Algorithms on SIMD and GPU Architectures

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