EP3241185A1 - Détection d'objets mobiles dans des vidéos - Google Patents

Détection d'objets mobiles dans des vidéos

Info

Publication number
EP3241185A1
EP3241185A1 EP14909406.2A EP14909406A EP3241185A1 EP 3241185 A1 EP3241185 A1 EP 3241185A1 EP 14909406 A EP14909406 A EP 14909406A EP 3241185 A1 EP3241185 A1 EP 3241185A1
Authority
EP
European Patent Office
Prior art keywords
frames
moving object
background
objective function
dimensional image
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
EP14909406.2A
Other languages
German (de)
English (en)
Other versions
EP3241185A4 (fr
Inventor
Xiaoli Li
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Nokia Technologies Oy
Original Assignee
Nokia Technologies Oy
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Nokia Technologies Oy filed Critical Nokia Technologies Oy
Publication of EP3241185A1 publication Critical patent/EP3241185A1/fr
Publication of EP3241185A4 publication Critical patent/EP3241185A4/fr
Withdrawn legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/20—Analysis of motion
    • G06T7/262—Analysis of motion using transform domain methods, e.g. Fourier domain methods
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/194—Segmentation; Edge detection involving foreground-background segmentation
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/20—Analysis of motion
    • G06T7/215—Motion-based segmentation
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20076—Probabilistic image processing
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30232—Surveillance
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30241—Trajectory

Definitions

  • the present disclosure generally relates to video processing, and more specifically, to moving object detection in videos.
  • Detecting moving objects such as persons, automobiles and the like in the video plays an important role in video analysis such as intelligent video surveillance, traffic monitoring, vehicle navigation, and human-machine interaction.
  • video analysis the outcome of moving object detection can be input into the modules like object recognition, object tracking, behavior analysis or the like for further processing. Therefore, high performance of moving object detection is a key for successful video analysis.
  • the detection of the background is a fundamental problem.
  • the detection accuracy is limited due to the changing background. More specifically, if the background of the video scene includes water ripples or waving trees, the detection of moving objects is prone to error.
  • the illumination variation, camera motion, and/or other kinds of noises in the background may also put negative effects on the moving object detection. Due to the changes of the background, in the conventional solutions, parts of the background might be classified as moving objects, while parts of foreground might be classified as background.
  • embodiments of the present invention provide a solution for moving object detection in the videos.
  • one embodiment of the present invention provides a computer-implemented method.
  • the method comprises: transforming a plurality of frames in a video from an initial image space to a high dimensional image space in a non-linear way; modeling background of the plurality of frames in the high dimensional image space; and detecting a moving object in the plurality of frames based on the modeling of the background of the plurality of frames in the high dimensional image space.
  • one embodiment of the present invention provides a computer-implemented apparatus.
  • the apparatus comprises: an image transformer configured to transform a plurality of frames in a video from an initial image space to a high dimensional image space in a non-linear way; a modeler configured to model background of the plurality of frames in the high dimensional image space; and a moving object detector configured to detect a moving object in the plurality of frames based on the modeling of the background of the plurality of frames in the high dimensional image space.
  • the frames in the videos may be transformed into a very high dimensional image space.
  • the non-linear model which is more powerful for describing complex factors such as changing background, changing background, illumination variation, camera motion, noise and the like
  • embodiments of the present invention is more robust and accurate to detect moving objects under the complex situations. Additionally, embodiments of the present invention achieve less false alarms and high detection rate.
  • FIG. 1 shows a flowchart of a method of detecting moving objects in a video according to one embodiment of the present invention
  • FIGs. 2A-2C show the results of moving object detection obtained by a conventional approach and one embodiment of the present invention
  • FIG. 3 shows a block diagram of an apparatus of detecting moving objects in a video according to one embodiment of the present invention.
  • FIG. 4 shows a block diagram of an example computer system suitable for implementing example embodiments of the present invention.
  • the term “includes” and its variants are to be read as open terms that mean “includes, but is not limited to. ”
  • the term “or” is to be read as “and/or” unless the context clearly indicates otherwise.
  • the term “based on” is to be read as “based at least in part on. ”
  • the term “one implementation” and “an implementation” are to be read as“at least one implementation. ”
  • the term “another implementation” is to be read as “at least one other implementation. ”
  • the terms “first, ” “second, ” “third” and the like may be used to refer to different or same objects. Other definitions, explicit and implicit, may be included below.
  • Example embodiments of the present invention model the background of the frames in the videos using a non-linear model.
  • a nonlinear model which is better than the linear one in the sense of describing the complex factors, the accuracy and performance of moving object detection in the videos can be improved.
  • the non-linear modeling of the background is achieved by transforming or mapping the original frames or images of the video being processed into a higher dimensional space.
  • the non-linear modeling of the initial background can be done effectively and efficiently.
  • the input of the moving object detection is a sequence of frames or images in the video, denoted as where represents vectorized image, n represents the number of pixels in a frame, T represents the number of frames being taken into consideration.
  • image and “frame” can be used interchangeably.
  • the goal is to find the positions of a moving object (s) or foreground in the frame x t .
  • the terms “foreground” and “moving object” can be used interchangeably.
  • the position of foreground location is represented by a foreground-indicator vector s ⁇ ⁇ 0,1 ⁇ n .
  • the pixel value of the foreground can be determined according to the foreground-indicator vector:
  • P s represents a foreground-extract operator.
  • the foreground-extract operator can be expressed as The pixel value of the background can also be determined according to the foreground-indicator vector:
  • FIG. 1 shows the flowchart of a method 100 of detecting moving object in a video.
  • the video may be of any suitable format.
  • the video may be compressed or encoded by any suitable technologies, either currently known or to be developed in the future.
  • the method 100 is entered at step 110, where a plurality of frames [x t-T , x t-T-1 , ... , x t-2 , x t-1 , x t ] in the video are transformed into a high dimensional image space in a non-linear way.
  • the dimension m of the high dimensional image space can be very high. Theoretically, the dimension can be even infinite.
  • the value of m can be selected such that m is much greater than the number of pixels in each frame. In this way, the non-linear correlations among the frames in the low dimensional image space can be better characterized and modeled.
  • mapping function denoted as ⁇
  • ⁇ any suitable mapping functions can be used in connection with embodiments of the present invention.
  • the mapping function satisfying the Mercer’s theorem can be used to guarantee the compactness and convergence of the transform.
  • the frames in the initial image space is transformed into the high dimensional image space, thereby obtaining a plurality of transformed frames [ ⁇ (x t-T ) , ... , ⁇ (x t-1 ) , ⁇ (x t ) ] .
  • the transformed frames [ ⁇ (x t-T ) , ... , ⁇ (x t-1 ) , ⁇ (x t ) ] may be linear and can thus be more easily described, which will be discussed below.
  • the transformed frames [ ⁇ (x t-T ) , ... , ⁇ (x t-1 ) , ⁇ (x t ) ] are not necessarily linear in the high dimensional image space.
  • the scope of the invention is not limited in this regard.
  • the frames can be transformed into the high dimensional image space without explicitly defining the mapping function.
  • the transformed frames and the modeling thereof can be described by use of proper kernel functions. Example embodiment in this regard will be discussed below.
  • step 120 the background of the plurality of frames [x t-T , x t-T-1 , ... , x t-2 , x t-1 , x t ] is modeled in the high dimensional image space.
  • the background of the frames is assumed to follow the Gaussian distribution and therefore is modeled by a linear transformation matrix where d represents the number of bases and u i is the i-th base vector.
  • d represents the number of bases
  • u i is the i-th base vector.
  • the initial frames are transformed into the high dimensional image space at step 110 and modeled in the image space with a very high dimension at step 120.
  • the non-linear modeling of the background of the initial frames is achieved.
  • the correlations of the frames can be better characterized to thereby identify the background and foreground (moving objects) more accurately.
  • the transformed frames may be linear in the high dimensional image space in one embodiment, as described above.
  • the base vector u j may be calculated as a linear sum of the background of the transformed frames as follows:
  • the non-linear modeling of background of the flames is achieved by modeling or approximating the background of the transformed flames using a linear model in the high dimensional image space.
  • modeling the background of the transformed frame using a linear model in me high dimensional image space would be beneficial in terms of operation efriciency and computation complexity. However, this is not necessarily required.
  • the background of the transformed frames can be approximated using any non-linear model in the high dimensional image space.
  • step 130 one or more moving objects (foreground) are detected based on the modeling of the background of the frames in the high dimensional image space.
  • an objective function can be defined based on the modeling at step 120. More specifically, the objective function at least characterizes the error in the modeling or approximation of background of the frames.
  • the objective function may be defined as follows:
  • the area of the foreground may be taken into consideration.
  • the area of the moving object in each frame is below a predefined threshold because a too large moving objection would probably means inaccurate detection.
  • the area term can be given by:
  • the connectivity of the moving object across the plurality of frames can be considered. It would be appreciated that the trajectory of a moving object is usually continuous between two consecutive frames.
  • the connectivity may be defined as follows:
  • N (i) is the set of neighbors of the pixel i.
  • the modeling error, foreground area and the connectivity can be combined together to define the objective function as follows:
  • the background of the frames can be detected by minimizing the objective function.
  • the foreground-indicator vector s, coefficient and the low-dimensional representation y i that can minimize the objective function L.
  • the kernel functions associated with the high dimensional image space can be used to solve this optimization problem.
  • KPCA Kernel Principal Component Analysis
  • the kernel function k (x i , x j ) can be in any form as long as the resulting kernel matrix K is semi-definite.
  • an example of the kernel function is shown as follows:
  • ⁇ is parameter which can be selected empirically. It is to be understood that the kernel functions shown in equation (15) is given merely for the purpose of illustration, without suggesting any limitations as to the scope of the invention. In other embodiments, any suitable kernel functions such as gaussian kernel function, radial basis function, and the like can be used as well.
  • the optimization of the objective function can be achieved by solving the following eigen-decomposition problem:
  • ⁇ and ⁇ represent the eigenvalue and eigenvector, respectively. It would be appreciated that there are totally d eigenvalues ⁇ 1 , ... , ⁇ d . In one embodiment, the eigenvalues may be sorted in ascending order, such that ⁇ 1 > ⁇ 2 >... ⁇ d .
  • the eigenvector ⁇ i corresponds to the eigenvalues ⁇ i .
  • the j entry of ⁇ i is
  • y i is the background of the initial frames in the low dimensional image space.
  • y i is expressed as follows:
  • Equation (19) can be calculated by the kernel function because
  • equation (19) can be formulated as terms in the form of kernel function as follows:
  • the foreground and background parts in the frames can be identified or indicated by the foreground indicator s which is defined in equation (1) , for example.
  • the objective function at least in part by the foreground indicator. That is, by means of the kernel functions, the objective function can be associated with the foreground indicator related to each pixel in each of the plurality of frames, where the foreground indicator indicates whether the related pixel belongs to the moving object (foreground) .
  • the kernel function is in the form of equation (15) .
  • the kernel function can be approximated by:
  • Equation (19) L background as defined in equation (19) can be expressed as follows:
  • the objective function is in the form of equation (13) . That is, in addition to L background , the objective function also includes the terms related to the area and connectivity of the moving object (s) . Based on equations (25) and (13) , the objective function L can be written as:
  • equation (26) is in a standard form of graph cuts.
  • the optimal solution s can be efficiently obtained.
  • the method 100 can be implemented by the pseudo code shown in the following table.
  • embodiments of the present invention is more robust and accurate to detect moving objects under the complex situations.
  • the proposed approach achieves less false alarms and high detection rate.
  • FIGs. 2A-2C show an example of moving object detection.
  • FIG. 2A shows frame in a video which has dynamic rain.
  • FIG. 2B is the result of a conventional approach of moving object detection. It can be seen that in FIG. 2B, the spring is incorrectly classified as moving objects. On the contrary, in the result obtained by one embodiment of the present invention as shown in FIG. 2C, the spring was removed from the foreground and the moving person is correctly detected.
  • FIG. 3 shows a block diagram of a computer-implemented apparatus for moving object detection according to one embodiment of the present invention.
  • the apparatus 300 comprises an image transformer 310 configured to transform a plurality of frames in a video from an initial image space to a high dimensional image space in a non-linear way; a modeler 320 configured to model background of the plurality of frames in the high dimensional image space; and a moving object detector 330 configured to detect a moving object in the plurality of frames based on the modeling of the background of the plurality of frames in the high dimensional image space.
  • the dimension of the high dimensional image space is greater than the number of pixels in each of the plurality of frames.
  • the modeler 320 may comprise a non-linear modeler 325 configured to model background of a plurality of transformed frames using a linear model in the high dimensional image space, the plurality of transformed frames obtained by transforming the plurality of frames in the non-linear way.
  • the apparatus 300 may further comprise an objective function controller 340 configured to determine an objective function characterizing an error of the modeling of the background of the plurality of frames.
  • the moving object detector 330 is configured to detect the moving object based on the objective function.
  • the objective function may further characterize at least one of: areas of the moving object in the plurality of frames, and connectivity of the moving object across the plurality of frames.
  • the apparatus 300 may further comprise a kernel function controller 350 configured to determine a set of kernel functions associated with the high dimensional image space.
  • the objective function controller 340 is configured to associate at least a part of the objective function and the background of the plurality of frames using the set of kernel functions, and the moving object detector 330 is configured to detect the moving object by minimizing the objective function.
  • the objective function controller 340 is configured to associate the objective function with a foreground indicator related to each pixel in each of the plurality of frames using the set of kernel functions, where the foreground indicator indicates whether the related pixel belongs to the moving object.
  • FIG. 4 shows a block diagram of an example computer system 400 suitable for implementing example embodiments of the present invention.
  • the computer system 400 can be a fixed type machine such as a desktop personal computer (PC) , a server, a mainframe, or the like.
  • the computer system 400 can be a mobile type machine such as a mobile phone, tablet PC, laptop, intelligent phone, personal digital assistance (PDA) , or the like.
  • PC personal computer
  • PDA personal digital assistance
  • the computer system 400 comprises a processor such as a central processing unit (CPU) 401 which is capable of performing various processes in accordance with a program stored in a read only memory (ROM) 402 or a program loaded from a storage unit 408 to a random access memory (RAM) 403.
  • a processor such as a central processing unit (CPU) 401 which is capable of performing various processes in accordance with a program stored in a read only memory (ROM) 402 or a program loaded from a storage unit 408 to a random access memory (RAM) 403.
  • ROM read only memory
  • RAM random access memory
  • data required when the CPU 401 performs the various processes or the like is also stored as required.
  • the CPU 401, the ROM 402 and the RAM 403 are connected to one another via a bus 404.
  • An input/output (I/O) interface 405 is also connected to the bus 404.
  • the following components are connected to the I/O interface 405: an input unit 406 including a keyboard, a mouse, or the like; an output unit 407 including a display such as a cathode ray tube (CRT) , aliquid crystal display (LCD) , or the like, and a loudspeaker or the like; the storage unit 408 including a hard disk or the like; and a communication unit 409 including a network interface card such as a LAN card, a modem, or the like. The communication unit 409 performs a communication process via the network such as the internet.
  • a drive 410 is also connected to the I/O interface 405 as required.
  • a removable medium 411 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like, is mounted on the drive 410 as required, so that a computer program read therefrom is installed into the storage unit 408 as required.
  • embodiments of the present invention comprise a computer program product including a computer program tangibly embodied on a machine readable medium, the computer program including program code for performing the method 100 and/or the pseudo code shown in Table 1.
  • the computer program may be downloaded and mounted from the network via the communication unit 409, and/or installed from the removable medium 411.
  • the functionally described herein can be performed, at least in part, by one or more hardware logic components.
  • illustrative types of hardware logic components include Field-programmable Gate Arrays (FPGAs) , Application-specific Integrated Circuits (ASICs) , Application-specific Standard Products (ASSPs) , System-on-a-chip systems (SOCs) , Complex Programmable Logic Devices (CPLDs) , and the like.
  • Various embodiments of the invention may be implemented in hardware or special purpose circuits, software, logic or any combination thereof. Some aspects may be implemented in hardware, while other aspects may be implemented in firmware or software which may be executed by a controller, microprocessor or other computing device. While various aspects of embodiments of the present invention are illustrated and described as block diagrams, flowcharts, or using some other pictorial representation, it will be appreciated that the blocks, apparatus, systems, techniques or methods described herein may be implemented in, as non-limiting examples, hardware, software, firmware, special purpose circuits or logic, general purpose hardware or controller or other computing devices, or some combination thereof.
  • embodiments of the present invention can be described in the general context of machine-executable instructions, such as those included in program modules, being executed in a device on a target real or virtual processor.
  • program modules include routines, programs, libraries, objects, classes, components, data structures, or the like that perform particular tasks or implement particular abstract data types.
  • the functionality of the program modules may be combined or split between program modules as desired in various implementations.
  • Machine-executable instructions for program modules may be executed within a local or distributed device. In a distributed device, program modules may be located in both local and remote storage media.
  • Program code for carrying out methods of the invention may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowcharts and/or block diagrams to be implemented.
  • the program code may execute entirely on a machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
  • a machine readable medium may be any tangible medium that may contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
  • the machine readable medium may be a machine readable signal medium or a machine readable storage medium.
  • a machine readable medium may include but not limited to an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing.
  • machine readable storage medium More specific examples of the machine readable storage medium would include an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM) , a read-only memory (ROM) , an erasable programmable read-only memory (EPROM or Flash memory) , an optical fiber, a portable compact disc read-only memory (CD-ROM) , an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
  • RAM random access memory
  • ROM read-only memory
  • EPROM or Flash memory erasable programmable read-only memory
  • CD-ROM portable compact disc read-only memory
  • magnetic storage device or any suitable combination of the foregoing.

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Multimedia (AREA)
  • Mathematical Physics (AREA)
  • Image Analysis (AREA)

Abstract

La présente invention se rapporte à une détection d'objets mobiles dans des vidéos. Selon un mode de réalisation, une pluralité de trames dans une vidéo sont transformées en un espace d'image de grande dimension de manière non linéaire. Ensuite, l'arrière-plan de la pluralité de trames peut être modélisé dans l'espace d'image de grande dimension. L'avant-plan ou l'objet mobile peut être détecté dans la pluralité de trames sur la base de la modélisation de l'arrière-plan dans l'espace d'image de grande dimension. Par utilisation du modèle non linéaire qui est le plus puissant pour décrire des facteurs complexes tels qu'un changement d'arrière-plan, une variation d'illumination, un mouvement d'appareil de prise de vues, un bruit et analogue, des modes de réalisation de la présente invention sont plus robustes et précis pour détecter des objets mobiles dans des situations complexes.
EP14909406.2A 2014-12-30 2014-12-30 Détection d'objets mobiles dans des vidéos Withdrawn EP3241185A4 (fr)

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