Real-time optical flow processing on embedded GPU: an hardware-aware algorithm to implementation strategy

Seznec, Mickael; Gac, Nicolas; Orieux, François; Naik, Alvin Sashala

doi:10.1007/s11554-021-01187-8

Cited by 5 publications

(4 citation statements)

References 27 publications

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“…A high-performance embedded processing platform based on a graphics processing unit (GPU), DSP, and FPGA has become the potential solution for onboard image processing [31][32][33][34]. As the specialized image processor, the embedded GPU processing platform [35] has been widely used in unmanned driving technology, AI computation, and video image processing. Its parallel processing capability supports it in handling complex data and geometry computing [36].…”

Section: Related Workmentioning

confidence: 99%

Implementation of Real-Time Space Target Detection and Tracking Algorithm for Space-Based Surveillance

Chen

Liu

et al. 2023

Remote Sensing

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Space-based target surveillance is important for aerospace safety. However, with the increasing complexity of the space environment, the stellar target and strong noise interference pose difficulties for space target detection. Simultaneously, it is hard to balance real-time processing with computational performance for the onboard processing platform owing to resource limitations. The heterogeneous multi-core architecture has corresponding processing capabilities, providing a hardware implementation platform with real-time and computational performance for space-based applications. This paper first developed a multi-stage joint detection and tracking model (MJDTM) for space targets in optical image sequences. This model combined an improved local contrast method and the Kalman filter to detect and track the potential targets and use differences in movement status to suppress the stellar targets. Then, a heterogeneous multi-core processing system based on a field-programmable gate array (FPGA) and digital signal processor (DSP) was established as the space-based image processing system. Finally, MJDTM was optimized and implemented on the above image processing system. The experiments conducted with simulated and actual image sequences examine the accuracy and efficiency of the MJDTM, which has a 95% detection probability while the false alarm rate is 10−4. According to the experimental results, the algorithm hardware implementation can detect targets in an image with 1024 × 1024 pixels in just 22.064 ms, which satisfies the real-time requirements of space-based surveillance.

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Section: Related Workmentioning

confidence: 99%

Implementation of Real-Time Space Target Detection and Tracking Algorithm for Space-Based Surveillance

Chen

Liu

et al. 2023

Remote Sensing

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“…The key to implementing the APSVD is the exact eigendecomposition, which can be achieved using the Symmetric Eigenvalue Divide (SYEVD) function based on QR decomposition or the Symmetric Eigenvalue Jacobi (SYEVJ) function based on Jacobi decomposition [46]. SYEVD employs a divide-and-conquer method to decompose a symmetric matrix into smaller sub-problems and solves them recursively.…”

Section: Apsvd Using Cudamentioning

confidence: 99%

“…To quantitatively analyze both methods, we introduce arithmetic intensity [46] which is a metric used to evaluate the performance of parallel computational tasks. Specifically, the arithmetic intensity I is defined as…”

Section: Apsvd Using Cudamentioning

confidence: 99%

Infrared Small-Target Detection Based on Background-Suppression Proximal Gradient and GPU Acceleration

Hao,

Liu,

Liu

et al. 2023

Remote Sensing

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Patch-based methods improve the performance of infrared small target detection, transforming the detection problem into a Low-Rank Sparse Decomposition (LRSD) problem. However, two challenges hinder the success of these methods: (1) The interference from strong edges of the background, and (2) the time-consuming nature of solving the model. To tackle these two challenges, we propose a novel infrared small-target detection method using a Background-Suppression Proximal Gradient (BSPG) and GPU parallelism. We first propose a new continuation strategy to suppress the strong edges. This strategy enables the model to simultaneously consider heterogeneous components while dealing with low-rank backgrounds. Then, the Approximate Partial Singular Value Decomposition (APSVD) is presented to accelerate solution of the LRSD problem and further improve the solution accuracy. Finally, we implement our method on GPU using multi-threaded parallelism, in order to further enhance the computational efficiency of the model. The experimental results demonstrate that our method out-performs existing advanced methods, in terms of detection accuracy and execution time.

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“…Recent works present real-time implementation of HORN & SCHUNCK and other more recent optical flow algorithms on GPU [4] and on FPGA [5], but not on CPU. Indeed, these are more often used as a comparison reference for other architectures and the optimizations details are not described [6].…”

Section: Related Workmentioning

confidence: 99%

Real-Time and Approximate Iterative Optical Flow Implementation on Low-Power Embedded CPUs

Millet,

Cassagne,

Rambaux

et al. 2023

2023 IEEE 34th International Conference on Application-Specific Systems, Architectures and Processors (ASAP)

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Optical flow estimation is used in many embedded computer vision applications, and it is known to be computationally intensive. In the literature, many methods exist to estimate optical flow. Thus, the challenge is to find a method that matches the applicative constraints. In an embedded system, a trade-off between power consumption and execution time has to be made to meet both energy and framerate constraints. This work proposes methods to implement an approximate HORN & SCHUNCK optical flow estimation that meets embedded CPUs constraints. This is achieved thanks to architectural optimizations, software optimizations and algorithm tuning. For instance, on the NVIDIA Jetson Nano, and for HD video sequences, the achieved frame latency is 12 ms for 5 Watts. To the best of our knowledge, this is the fastest optical flow implementation on embedded CPUs.

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Real-time optical flow processing on embedded GPU: an hardware-aware algorithm to implementation strategy

Cited by 5 publications

References 27 publications

Implementation of Real-Time Space Target Detection and Tracking Algorithm for Space-Based Surveillance

Implementation of Real-Time Space Target Detection and Tracking Algorithm for Space-Based Surveillance

Infrared Small-Target Detection Based on Background-Suppression Proximal Gradient and GPU Acceleration

Real-Time and Approximate Iterative Optical Flow Implementation on Low-Power Embedded CPUs

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