WO2005050342A2 - Device for estimating optical flow in images using fpgas - Google Patents

Device for estimating optical flow in images using fpgas Download PDF

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Publication number
WO2005050342A2
WO2005050342A2 PCT/ES2004/000523 ES2004000523W WO2005050342A2 WO 2005050342 A2 WO2005050342 A2 WO 2005050342A2 ES 2004000523 W ES2004000523 W ES 2004000523W WO 2005050342 A2 WO2005050342 A2 WO 2005050342A2
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Prior art keywords
optical flow
image
estimating
images
processing unit
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PCT/ES2004/000523
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Spanish (es)
French (fr)
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WO2005050342A3 (en
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Eduardo Ros Vidal
Sonia MOTA FERNÁNDEZ
Antonio Javier DÍAZ ALONSO
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Universidad De Granada
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Publication of WO2005050342A3 publication Critical patent/WO2005050342A3/en

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/50—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding
    • H04N19/503—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding involving temporal prediction
    • H04N19/51—Motion estimation or motion compensation
    • H04N19/537—Motion estimation other than block-based
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/20—Analysis of motion
    • G06T7/269—Analysis of motion using gradient-based methods

Definitions

  • the present invention is framed within the devices for real-time image processing. More specifically within digital devices for estimating optical flow in digital images at intensity levels.
  • the optical flow in image sequences consists of, by different methods, estimating the displacement of the gray levels of an image. This displacement is measured at the sub-pixel level and allows us to determine the speed of the displacement of the pixels of an image. This information allows the determination of moving objects through vision and its segmentation or monitoring.
  • the knowledge of this map of image speeds is currently used in mobile object segmentation, tracking, 3-D reconstruction of scenes and video coding / compression systems, its potential utilities being very numerous. According to this situation, there are numerous patented methodologies, mainly in the United States, that describe new methods of computing the optical flow. They basically describe different computer implementations of the system, each trying to improve some of the typical problems that others suffer.
  • the systems described above although they have methods and "devices" for estimating the optical flow, do not pursue the development of real-time systems.
  • the main problem is the estimation of the optical flow is the high computing power required for its processing, which normally relegates the devices based on image processing to the background in real applications. They are also not portable systems, which significantly limits their usefulness for real applications, or their use as embedded systems.
  • the device invented is intended to estimate dense optical flow in digital video sequences.
  • the digital sensor can be of any type, standard video, infrared, radar etc.
  • the processing will be done in real time using an optical flow estimation method based on the gradients of the images.
  • the existence of circuits capable of performing such an operation is very small, being even less numerous those that can be used as embedded systems.
  • the present invention is capable to perform such a task based on a structure for FPGA circuits of great parallelism.
  • the data entry of the system will be the images captured with any opto-electronic sensor and digitized.
  • the input image is stored in an external memory for later reading, as shown by the module called "Frame-Grabber" (or capture module) of Figure 1.
  • the device computes the temporal derivative from space-softened images. temporarily, using temporary filtering with IIR filters.
  • the device can operate in different configurations that are determined by an especially existing input signal.
  • CONTROL is responsible for modifying the operation of the remaining elements of the circuit according to the state of the circuit.
  • the different configurations modify the work image sizes, and the flow estimation can be made at different spatial scales. It is also possible to modify different model parameters such as confidence thresholds in the estimation of speeds or derivatives. The choice of these parameters will allow to choose the density of estimates present in the image and as a consequence the Skill threshold thereof.
  • the device uses external memory to store previous results, used to estimate the speed recursively in a more stable way.
  • figure 1 shows a basic scheme of the main processing elements performed within the circuit of the invention, also indicating the data input and output lines.
  • the blocks shown report the basic processing and control units described in the previous section.
  • the circuit made has the inputs shown in Figure 1, the values of the gray levels of the digital image, the control input and the interconnections with the external memory.
  • the output provides the pixel speed estimates of the image.

Abstract

The invention relates to a device which is designed to estimate the dense optical flow of a video sequence in real time and which supplies the velocities of the movement of the grey levels present in the sequence. According to the invention, the digitised images are used as input for the system. As shown in the figure, the processing is performed by the inventive device using an architecture that is based on a computation of the space-time gradients of the sequence (derivatives module). The invention also comprises a control unit (control module) which can be used to configure the operation of the unit. In addition, the device supplies the velocity of each pixel (velocity computation module). The device is suitable for systems that are used for mobile object segmentation, tracking, 3D scene reconstruction, video compression, etc., in which all of the processing is performed in real time, said system being embedded and, consequently, portable.

Description

DISPOSITIVO PARA LA ESTIMACIÓN DE FLUJO ÓPTICO EN IMÁGENES MEDIANTE FPGAS DEVICE FOR ESTIMATING OPTICAL FLOW IN IMAGES BY FPGAS
SECTOR DE LA TÉCNICASECTOR OF THE TECHNIQUE
La presente invención se encuadra dentro de los dispositivos para procesamiento de imágenes en tiempo real. Más concretamente dentro de los dispositivos digitales para estimación de flujo óptico en imágenes digitales en niveles de intensidad.The present invention is framed within the devices for real-time image processing. More specifically within digital devices for estimating optical flow in digital images at intensity levels.
ESTADO DE LA TÉCNICASTATE OF THE TECHNIQUE
El flujo óptico en secuencias de imágenes consiste en, mediante diferentes métodos, estimar el desplazamiento de los niveles de gris de una imagen. Este desplazamiento es medido a nivel subpíxel y nos permite determinar la velocidad del desplazamiento de los píxeles de una imagen. Esta información permite la determinación de objetos en movimiento mediante visión y su segmentación o seguimiento. El conocimiento de este mapa de velocidades de la imagen es utilizado en la actualidad en sistemas de segmentación de objetos móviles, seguimiento, reconstrucción 3-D de escenas y codificación / compresión de video, siendo sus potenciales utilidades muy numerosas. Acorde a esta situación, existen numerosas metodologías patentadas, principalmente en Estados Unidos, que describen nuevos métodos de cómputo del flujo óptico. En ellos se describen básicamente diferentes implementaciones computacionales del sistema, cada una intentando mejorar algunos de los problemas típicos que padecen las demás. Como ejemplo y referencia tenemos el documento WO 01/96982 A2, que describe un método basado en gradiente y pirámide multiescala para estimación de flujo óptico mediante el algoritmo de Horn & Schunck (descrito en el artículo Determining Optical flow" publicado en "Artificial Intelligence" 1981, vol 17, pp 185-203). Otros documentos basados en los métodos multiescala son US 6.370.196 Bl, US 5680487 (basado en gradiente) y FR2729811 (cómputo de velocidades mediante interpolación polinomial). Diferentes aproximaciones son los documentos que pretenden mejorar la estimación del flujo óptico mediante aproximaciones más robustas. Podemos usar un mapa disperso de rasgos fiables basados en la geometría epipolar de la cámara, documento US 20030086590 Al, o estimaciones multihipótesis, documento US 20030076982 Al. También existen métodos basados en estimaciones de la fiabilidad del flujo óptico como el descrito en US 20030058945 Al. Finalmente podemos encontrar documentos como el US 20030086590 Al que resuelve la estimación de flujo óptico mediante la solución de la ecuación de Poisson y un método de relajación.The optical flow in image sequences consists of, by different methods, estimating the displacement of the gray levels of an image. This displacement is measured at the sub-pixel level and allows us to determine the speed of the displacement of the pixels of an image. This information allows the determination of moving objects through vision and its segmentation or monitoring. The knowledge of this map of image speeds is currently used in mobile object segmentation, tracking, 3-D reconstruction of scenes and video coding / compression systems, its potential utilities being very numerous. According to this situation, there are numerous patented methodologies, mainly in the United States, that describe new methods of computing the optical flow. They basically describe different computer implementations of the system, each trying to improve some of the typical problems that others suffer. As an example and reference we have WO 01/96982 A2, which describes a gradient-based method and multiscale pyramid for estimation of optical flow using the Horn & Schunck algorithm (described in the article Determining Optical flow "published in" Artificial Intelligence " 1981, vol 17, pp 185-203) Other documents based on multiscale methods are US 6,370,196 Bl, US 5680487 (gradient-based) and FR2729811 (computation of speeds by polynomial interpolation). Different approaches are the documents intended improve the estimation of the optical flow by more robust approaches We can use a scattered map of reliable features based on the epipolar geometry of the camera, document US 20030086590 Al, or multi-hypothesis estimates, document US 20030076982 Al. There are also methods based on estimates of the reliability of the optical flow as described in US 20030058945 Al. Finally we can find documents such as e l US 20030086590 To which the estimation of optical flow is solved by solving the Poisson equation and a relaxation method.
Los sistemas anteriormente descritos, si bien presentan métodos y "dispositivos" para la estimación del flujo óptico, no persiguen el desarrollo de sistemas en tiempo real. El principal problema es la estimación del flujo óptico es la elevada potencia de calculo requerida para su procesamiento lo que, normalmente relega a los dispositivos basados en procesamiento de imágenes a un segundo plano en las aplicaciones reales. Además no son sistemas portables, lo que limita significativamente su utilidad para aplicaciones reales, o su utilización como sistemas embebidos.The systems described above, although they have methods and "devices" for estimating the optical flow, do not pursue the development of real-time systems. The main problem is the estimation of the optical flow is the high computing power required for its processing, which normally relegates the devices based on image processing to the background in real applications. They are also not portable systems, which significantly limits their usefulness for real applications, or their use as embedded systems.
Existe una metodología para la estimación del movimiento de los niveles de gris basado en correlación entre bloques de la imagen y conocido en la terminología anglosajona como métodos de "blockmaching". Basados en esta técnica sí que existen diferentes dispositivos hardware que son capaces estimar el moviendo de los bloques de la imagen en tiempo real. La principal utilidad de esta técnica es la compresión de video, siendo muy usada en estándares tales como el mpeg y afines. Ejemplos de ello son los dispositivos descritos en W09526539, US 5969772 (permite además detección de objetos en movimiento), EP0577418 A2 (para codificación de video), Patente US 5627591 (usa un mapa disperso basado en bordes para estimar el movimiento y codificar video), US 20030123551 (para codificación mpeg).There is a methodology for estimating the movement of gray levels based on correlation between blocks of the image and known in Anglo-Saxon terminology as "blockmaching" methods. Based on this technique, there are different hardware devices that are able to estimate the movement of the image blocks in real time. The main utility of this technique is video compression, being widely used in standards such as mpeg and related. Examples are the devices described in W09526539, US 5969772 (also allows detection of moving objects), EP0577418 A2 (for video coding), US Patent 5627591 (uses a scattered border based map to estimate motion and encode video) , US 20030123551 (for MPEG coding).
El problema de la técnica anterior es que si bien es muy adecuada para la compresión de video, la información del movimiento que proporciona no se corresponde siempre con los desplazamientos reales de los objetos en la imagen. Esto, que es un problema general de todas las aproximaciones para la estimación de flujo óptico, se hace especialmente crítico en los métodos de "blockmaching". Es por ello que para otras aplicaciones, en especial reconstrucción 3-D de la escena, los métodos basados en otras aproximaciones, típicamente métodos basados en cómputo del gradiente, son más apropiados. El problema de estos sistemas es su complejidad lo que hace poco frecuente la existencia de sistemas de procesamiento en tiempo real basados en ello. Un ejemplo destacable y que incluimos aquí como referencia es el descrito en el documento US5627905. En él se describe un dispositivo de estimación de flujo óptico basado en gradiente y procesado de selección de patrones de movimiento. El dispositivo permite también el seguimiento de objetos. En lo referente a su implementación hardware utiliza un sistema mixto procesador-PGAs así como diferentes chip de memoria.The problem of the prior art is that while it is very suitable for video compression, the movement information it provides does not always correspond to the actual displacements of the objects in the image. This, which is a general problem of all approaches to the estimation of optical flow, is especially critical in the "blockmaching" methods. That is why for other applications, especially 3-D reconstruction of the scene, methods based on other approaches, typically methods based on gradient computation, are more appropriate. The problem with these systems is their complexity, which makes the existence of real-time processing systems based on it infrequent. A notable example and which we include here as a reference is the one described in document US5627905. It describes an optical flow estimation device based on gradient and motion pattern selection processing. The device also allows object tracking. Regarding its hardware implementation, it uses a mixed processor-PGA system as well as different memory chips.
DESCRIPCIÓN DETALLADA DE LA INVENCIÓN El dispositivo inventado tiene por objeto la estimación de flujo óptico denso en secuencias de video digital. El sensor digital podrá ser de cualquier tipo, video estándar, infrarrojos, radar etc. El procesamiento será realizado en tiempo real utilizando un método de estimación de flujo óptico basado en los gradientes de las imágenes. Como hemos visto en el apartado anterior, la existencia de circuitos capaces de realizar tal operación es muy reducida, siendo aún menos numerosos los que pueden ser utilizados como sistemas embebidos. La presente invención es capaz de realizar tal tarea basándose en una estructura para circuitos tipo FPGA de gran paralelismo.DETAILED DESCRIPTION OF THE INVENTION The device invented is intended to estimate dense optical flow in digital video sequences. The digital sensor can be of any type, standard video, infrared, radar etc. The processing will be done in real time using an optical flow estimation method based on the gradients of the images. As we have seen in the previous section, the existence of circuits capable of performing such an operation is very small, being even less numerous those that can be used as embedded systems. The present invention is capable to perform such a task based on a structure for FPGA circuits of great parallelism.
La entrada de datos del sistema serán las imágenes capturadas con cualquier sensor opto-electrónico y digitalizadas. La imagen de entrada es almacenada en una memoria externa para su posterior lectura, tal y como muestra el modulo denominado "Frame-Grabber" (o módulo capturador) de la figura 1. El dispositivo computa la derivada temporal a partir de imágenes suavizadas espacio-temporalmente, utilizando para ello un filtrado temporal con filtros IIR.The data entry of the system will be the images captured with any opto-electronic sensor and digitized. The input image is stored in an external memory for later reading, as shown by the module called "Frame-Grabber" (or capture module) of Figure 1. The device computes the temporal derivative from space-softened images. temporarily, using temporary filtering with IIR filters.
El dispositivo puede operar en diferentes configuraciones que se determinan mediante una señal entrada especialmente existente para ello. El modulo de la figura 1 denominado CONTROL es el encargado de modificar la operación de los restantes elementos del circuito de acuerdo con el estado del mismo. Las diferentes configuraciones modifican los tamaños de imagen de trabajo, pudiendo realizarse la estimación de flujo a diferentes escalas espaciales. También es posible modificar diferentes parámetros del modelo como los umbrales de confianza en la estimación de velocidades o de derivadas. La elección de estos parámetros permitirá elegir la densidad de estimaciones presentes en la imagen y como consecuencia el umbral de Habilidad de los mismos.The device can operate in different configurations that are determined by an especially existing input signal. The module in Figure 1 called CONTROL is responsible for modifying the operation of the remaining elements of the circuit according to the state of the circuit. The different configurations modify the work image sizes, and the flow estimation can be made at different spatial scales. It is also possible to modify different model parameters such as confidence thresholds in the estimation of speeds or derivatives. The choice of these parameters will allow to choose the density of estimates present in the image and as a consequence the Skill threshold thereof.
Por último, se computa la velocidad. El dispositivo usa memoria externa para el almacenamiento de resultados previos, utilizado para estimar la velocidad recursivamente de manera más estable.Finally, speed is computed. The device uses external memory to store previous results, used to estimate the speed recursively in a more stable way.
BREVE DESCRIPCIÓN DE LA FIGURASBRIEF DESCRIPTION OF THE FIGURES
La única figura utilizada, figura 1, muestra un esquema básico de los principales elementos de procesamiento realizados dentro de circuito de la invención, indicando también las líneas de entrada y salida de datos. Los bloques mostrados informan de las unidades básicas de procesamiento y control descritas en el apartado anterior.The only figure used, figure 1, shows a basic scheme of the main processing elements performed within the circuit of the invention, also indicating the data input and output lines. The blocks shown report the basic processing and control units described in the previous section.
MODOS DE REALIZACIÓN DE LA INVENCIÓNEMBODIMENTS OF THE INVENTION
Para la realización del circuito de la presente invención, descrita ya su función en el apartado anterior, utilizamos un circuito tipo FPGA que cumpla las restricciones de tiempo real y capacidad de procesamiento descritas en la presente invención, sin perjuicio de otras soluciones como son las basadas en circuitos de uso especifico (ASIC).For the realization of the circuit of the present invention, already described its function in the previous section, we use an FPGA type circuit that meets the real time and processing capacity restrictions described in the present invention, without prejudice to other solutions such as those based in specific use circuits (ASIC).
El circuito realizado posee las entradas mostradas en la figura 1, los valores de los niveles de gris de la imagen digital, la entrada de control y las interconexiones con la memoria externa. La salida proporciona las estimaciones de velocidad de los píxel de la imagen. The circuit made has the inputs shown in Figure 1, the values of the gray levels of the digital image, the control input and the interconnections with the external memory. The output provides the pixel speed estimates of the image.

Claims

REIVINDICACIONES
1. Dispositivo para la estimación de flujo óptico en imágenes mediante FPGAs caracterizado por comprender los siguientes elementos: - Circuito de procesamiento de imágenes en tiempo real para estimación de flujo óptico mediante FPGA. - Entradas y/o salidas para el control del sistema, valores de los píxeles de la imagen, memorias externas y resultados obtenidos. - Unidad de transferencia de datos entre el dispositivo y la memoria externa. - Unidad de procesamiento para realizar el suavizado de las imágenes. - Unidad de procesamiento para estimación de las derivadas de la imagen. - Unidad de procesamiento para el cómputo de las velocidades de los píxeles de la imagen. - Unidad de control del sistema. Todo ello en un mismo chip (Sistema en Chip, SoC). Esto facilita cómputos complejos y evita cuellos de botella debidos a las comunicaciones. El circuito se ha desarrollado utilizando tecnología FPGA, lo cual provee de gran versatilidad al sistema, pudiendo ser particularizado fácilmente para distintas aplicaciones. 1. Device for estimating optical flow in images using FPGAs characterized by comprising the following elements: - Real-time image processing circuit for estimating optical flow using FPGA. - Inputs and / or outputs for system control, image pixel values, external memories and results obtained. - Data transfer unit between the device and external memory. - Processing unit for smoothing images. - Processing unit for estimating the derivatives of the image. - Processing unit for computing pixel image speeds. - System control unit. All this in the same chip (System in Chip, SoC). This facilitates complex computations and avoids bottlenecks due to communications. The circuit has been developed using FPGA technology, which provides the system with great versatility, and can be easily customized for different applications.
2. Dispositivo para estimación de flujo óptico según reivindicación 1 caracterizado por que la unidad de procesamiento para suavizado de imágenes realiza un suavizado recursivo de la imagen mediante un filtro tipo IIR.2. Device for estimating optical flow according to claim 1, characterized in that the image smoothing processing unit performs recursive image smoothing by means of a type IIR filter.
3. Dispositivo para estimación de flujo óptico según reivindicaciones anteriores caracterizado por que la unidad de procesamiento para cómputo de velocidades estima las mismas de manera recursiva, utilizando los valores obtenidos en las imágenes anteriores.3. Device for estimating optical flow according to previous claims characterized in that the processing unit for speed calculation estimates them recursively, using the values obtained in the previous images.
4. Dispositivo para estimación de flujo óptico según reivindicaciones caracterizado por que la unidad de control del sistema permite seleccionar la escala espacial para el cómputo de flujo óptico, así como modificar los umbrales de velocidades computables y de fiabilidad en su estimación. 4. Device for estimating optical flow according to claims characterized in that the control unit of the system allows to select the spatial scale for the computation of optical flow, as well as to modify the thresholds of computable speeds and reliability in its estimation.
5. Dispositivo para estimación de flujo óptico según reivindicaciones anteriores caracterizado por que el computo de flujo óptico es realizado utilizando una técnica basada en cómputo de gradientes de la imagen, obteniéndose un flujo óptico denso. 5. Device for estimating optical flow according to previous claims characterized in that the optical flow computation is performed using a technique based on the calculation of image gradients, obtaining a dense optical flow.
PCT/ES2004/000523 2003-11-24 2004-11-23 Device for estimating optical flow in images using fpgas WO2005050342A2 (en)

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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE19712017A1 (en) * 1997-03-15 1998-09-17 Gmd Gmbh Compact sensor system for optical motion detection in real time
US6215898B1 (en) * 1997-04-15 2001-04-10 Interval Research Corporation Data processing system and method
US20020106120A1 (en) * 2001-01-31 2002-08-08 Nicole Brandenburg Method of analyzing in real time the correspondence of image characteristics in corresponding video images

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE19712017A1 (en) * 1997-03-15 1998-09-17 Gmd Gmbh Compact sensor system for optical motion detection in real time
US6215898B1 (en) * 1997-04-15 2001-04-10 Interval Research Corporation Data processing system and method
US20020106120A1 (en) * 2001-01-31 2002-08-08 Nicole Brandenburg Method of analyzing in real time the correspondence of image characteristics in corresponding video images

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