Change-driven data flow image processing architecture for optical flow computation

Sosa, Julio C.; Boluda, Jose Antonio; Pardo, Fernando; Gomez-Fabela, Rocio

doi:10.1007/s11554-007-0060-y

Cited by 10 publications

(10 citation statements)

References 19 publications

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“…In recent years, there have also been many implementations based on FPGA [5,15,28,[45][46][47][48] and graphic processor units (GPU) [6,8,[49][50][51]. The results of a comparative study of both technologies for real-time optical flow computation are presented in [52].…”

Section: Parallelization Of the Optical Flowmentioning

confidence: 99%

Parallelization of the optical flow computation in sequences from moving cameras

et al. 2014

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This paper presents a flexible and scalable approach to the parallelization of the computation of optical flow. This approach is based on data parallel distribution. Images are divided into several subimages processed by a software pipeline while respecting dependencies between computation stages. The parallelization has been implemented in three different infrastructures: shared, distributed memory, and hybrid to show its conceptual flexibility and scalability. A significant improvement in performance was obtained in all three cases. These versions have been used to compute the optical flow of video sequences taken in adverse conditions, with a moving camera and natural-light conditions, on board a conventional vehicle traveling on public roads. The parallelization adopted has been developed from the analysis of dependencies presented by the well-known Lucas-Kanade algorithm, using a sequential version developed at the University of Porto as the starting point.

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Section: Parallelization Of the Optical Flowmentioning

confidence: 99%

Parallelization of the optical flow computation in sequences from moving cameras

et al. 2014

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“…General-purpose MIMD as the connection machine [15,16], networks of transputers [17], or cellular neural networks [18,19] were also used in the past. http://jivp.eurasipjournals.com/content/2014/1 /24 In recent years, there have also been many implementations based on field-programmable gate array (FPGA) [20][21][22] and graphic processor units (GPU) [23][24][25]. The results of a comparative study of both technologies for real-time optical flow computation are presented in [26].…”

Section: Parallelization Of Optical Flowmentioning

confidence: 99%

Locating moving objects in car-driving sequences

et al. 2014

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This paper presents a system for the search and detection of moving objects in a sequence of images previously captured by a camera installed in a conventional vehicle. The objective is the design and implementation of a software system based on optical flow analysis to detect and identify moving objects as perceived by a driver, taking into account that these objects could interfere with the driver's behavior, either through distraction or by posing an actual danger that may require an active response. The problem presents significant difficulties because the vehicle travels on conventional roads, open to normal traffic. Consequently, the scenes are recorded with natural lighting, i.e., under highly variable conditions (intensity, shadows, etc.). Furthermore, the use of a moving camera makes it difficult to properly identify static objects such as the road itself, signals, buildings, landscapes, and moving objects of the same speed, such as pedestrians or other vehicles. The proposed method consists of three stages. First, the optical flow is calculated for each image of the sequence, as a first estimate of the apparent motion. In a second step, two segmentation processes are addressed: the optical flow itself and the images of the sequence. Finally, in the last stage, the results of these two segmentation processes are combined to obtain the movement of the objects present in the sequence, identifying both their direction and magnitude. The quality of the results obtained with different sequences of real images makes this software suitable for systems to study driver behavior and to help detect danger situations, as various international traffic agencies consider in their research projects.

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“…Furthermore, the density of the generated optical flow decreases as the input image is sub-sampled. Sosa et al [31] use a change-driven data processing algorithm to generate the HS optical flow for pixels in large motion. Instead of all of the pixels in a full image, only pre-selected pixels are calculated for optical flow generation.…”

Section: Introductionmentioning

confidence: 99%

A Novel Hardware Architecture of the Lucas–Kanade Optical Flow for Reduced Frame Memory Access

Seong

Rhee

Lee

2016

IEEE Trans. Circuits Syst. Video Technol.

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Abstract-The Lucas-Kanade (LK) algorithm is a cost-efficient gradient-based algorithm for real-time optical flow generation. An excessive external memory access limits the LK algorithm from being used broadly in practical high-frame-rate applications. To overcome this limitation, this paper proposes a novel hardware architecture that stores the input image after the Gaussian filtering operation instead of the original input image itself. The Gaussian-filtered image is downsampled in both the horizontal and vertical directions, thus reducing the external memory access to one quarter of the original data. The downsampling operation does not cause a significant degradation of the accuracy because the Gaussian filter is a low-pass filter that reduces the aliasing effect of downsampling. The downsampled pixels are selected in an interleaved manner across multiple frames to reduce the degradation of the accuracy. Experimental results show that the proposed algorithm reduces the frame memory access by 61% to 75% when compared to the previous research.Index Terms-Lucas-Kanade optical flow, FPGA, VLSI, frame bandwidth 1051-8215 (c)

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Change-driven data flow image processing architecture for optical flow computation

Cited by 10 publications

References 19 publications

Parallelization of the optical flow computation in sequences from moving cameras

Parallelization of the optical flow computation in sequences from moving cameras

Locating moving objects in car-driving sequences

A Novel Hardware Architecture of the Lucas–Kanade Optical Flow for Reduced Frame Memory Access

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