EP4278329A1 - Procédé et système de reconnaissance d'objets représentés dans une image au moyen d'un nuage de points - Google Patents

Procédé et système de reconnaissance d'objets représentés dans une image au moyen d'un nuage de points

Info

Publication number
EP4278329A1
EP4278329A1 EP21843940.4A EP21843940A EP4278329A1 EP 4278329 A1 EP4278329 A1 EP 4278329A1 EP 21843940 A EP21843940 A EP 21843940A EP 4278329 A1 EP4278329 A1 EP 4278329A1
Authority
EP
European Patent Office
Prior art keywords
points
image
point
point cloud
probability density
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.)
Pending
Application number
EP21843940.4A
Other languages
German (de)
English (en)
Inventor
Niklas HERMES
Cornelius REINFELDT
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.)
Gestigon GmbH
Original Assignee
Gestigon GmbH
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 Gestigon GmbH filed Critical Gestigon GmbH
Publication of EP4278329A1 publication Critical patent/EP4278329A1/fr
Pending legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/26—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/20—Image enhancement or restoration by the use of local operators
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/762—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using clustering, e.g. of similar faces in social networks
    • G06V10/763—Non-hierarchical techniques, e.g. based on statistics of modelling distributions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/77—Processing image or video features in feature spaces; using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]; Blind source separation
    • G06V10/771—Feature selection, e.g. selecting representative features from a multi-dimensional feature space
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/60—Type of objects
    • G06V20/64—Three-dimensional objects
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/20—Movements or behaviour, e.g. gesture recognition
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V2201/00—Indexing scheme relating to image or video recognition or understanding
    • G06V2201/07—Target detection

Definitions

  • the present invention relates to a method and a system for recognizing one or more objects that are represented in an image or in corresponding image data using a point cloud.
  • the task arises of analyzing image data, ie data representing an image or a sequence of images, such as a video, to determine whether and, if so, which objects are depicted in the image(s).
  • image data ie data representing an image or a sequence of images, such as a video
  • the detection of movements or changes in such objects on the basis of such images or image data is also regularly of interest.
  • the methods for generating images or image data also include methods of, in particular discrete, scanning of a real scene with one or more associated real objects (e.g. people or things), where the resulting image data represents a two- or three-dimensional point cloud.
  • Such scanning can be carried out in particular with image sensors that also scan a scene in the depth dimension.
  • image sensors are, in particular, stereo cameras, time-of-flight sensors (time of flight or time-of-flight (TOF) sensors), and electro-optical distance sensors (laser range finders (LRF) sensors).
  • TOF time of flight or time-of-flight
  • LRF laser range finders
  • point clouds can also be generated by radar, lidar or ultrasonic sensors.
  • such point clouds can also be generated artificially, without a real scene having to be recorded by sensors.
  • such point clouds can be generated artificially, in particular computer-aided, as part of or as the result of simulations, in particular simulations of real scenes.
  • segment such a point cloud in the sense of image processing
  • segments i.e. image segments
  • a simple known method for such a foreground/background segmentation for an image given by a point cloud is the Evaluate depth information regarding the points of a point cloud by means of a threshold value method, in that all points which, according to their depth information, are closer than a specific depth threshold are assigned to the image foreground, while all other points are assigned to the image background.
  • a separation of the two objects in the image or in the point cloud can also be achieved in this way.
  • the object of the present invention is to further improve the recognition of one or more objects that are represented in an image or in corresponding image data using a cloud of points. In particular, it is desirable to achieve improved separability of different objects.
  • a “cloud of points” in the sense of the invention is a set of points of a vector space (unless restricted to specific dimensions below for embodiments) of any given dimension M>1, which in particular can have an organized or an unorganized spatial structure.
  • a point cloud is described by the points it contains, which can each be recorded in particular by their positions specified using spatial coordinates.
  • attributes such as B. geometric standards, color values, temperature values, recording times or measurement accuracies or other information.
  • a “one-dimensional quantity” within the meaning of the invention is to be understood as any selected quantity that can be completely determined one-dimensionally, ie as a number (with or without a unit), and that characterizes a property of a point in a point cloud.
  • the property can be position information, such as a spatial coordinate, or an attribute of the point or be derived therefrom.
  • the size can correspond in particular, but is not limited to, an assignment of the position to a specific point on a directional line (e.g. coordinate axis). In another example, however, it could also correspond to a distance of the respective point of the point cloud from a specific reference point, so that, for example, points lying concentrically at the same distance from this reference point have the same value for the size.
  • X be a continuous random variable (here a continuous variable representing one of the one-dimensional characteristic quantities).
  • a “one-dimensional probability density function” within the meaning of the invention is then to understand a mathematical function f(x) of the one-dimensional random variable X, for which the following applies: sp (ci ⁇ X ⁇ b) stands for the probability or actual frequency of the occurrence of a value for x from the value interval ]a;b] specified by a and b.
  • sp (ci ⁇ X ⁇ b) stands for the probability or actual frequency of the occurrence of a value for x from the value interval ]a;b] specified by a and b.
  • this definition of f(x) agrees with the usual mathematical definition of a probability density function of a one-dimensional continuous random variable.
  • the concept of a “one-dimensional probability density function” within the meaning of the invention is therefore generalized, since c can also assume values other than 1 here.
  • a “segment” of an image (or a point cloud) in the sense of the invention is a content-related region of an image (or a point cloud) that is defined by combining adjacent pixels (or points in a point cloud) according to a specific homogeneity criterion is.
  • the homogeneity criterion can relate in particular to a position or coordinate or an attribute of the points, without being limited thereto.
  • the context of the region can thus be understood spatially in some cases in particular, while in other cases it can relate in particular to points in the sense of the homogeneity criterion of the same or similar attributes.
  • the terms “comprises,” “includes,” “includes,” “has,” “has,” “having,” or any other variant thereof, as appropriate, are intended to cover non-exclusive inclusion.
  • a method or apparatus that includes or has a list of elements is not necessarily limited to those elements, but may include other elements that are not expressly listed or that are inherent in such method or apparatus.
  • the term "configured” or “set up” to perform a specific function (and respective modifications thereof) is to be understood within the meaning of the invention that the corresponding device is already in a configuration or setting in which it can or can perform the function it is at least adjustable - i.e. configurable - so that it can carry out the function after appropriate setting.
  • the configuration can take place, for example, via a corresponding setting of parameters of a process flow or of switches or the like for activating or deactivating functionalities or settings.
  • the device can have a plurality of predetermined configurations or operating modes, so that the configuration can take place by selecting one of these configurations or operating modes.
  • the aforementioned method according to the first aspect is therefore based in particular on describing the cloud of points using one or more selected, one-dimensional variables that characterize each point in the cloud of points on the basis of its position or properties, and a frequency distribution of the values of the to approximate the respective variable by means of one-dimensional probability density functions (in the sense of the approximation or adjustment calculation).
  • this point can then be unambiguously assigned to a segment of the image or the point cloud. In many cases, this is even possible if the point cloud portions of different objects or of one object and the image background are close to each other.
  • This can be used in particular to separate the images of multiple objects represented by a point cloud from one another.
  • the accuracy of the separation can be increased and the error rate reduced.
  • Particularly high accuracies or low error rates can be achieved in the case of m>1, since different variables that are independent of one another interact here to create even stricter separation criteria for assigning the points to an image segment and thus if necessary, to deliver to an associated object.
  • the points of the point cloud are assigned to one segment each (segmentation criterion) in such a way that each point to be assigned is assigned to a segment of the image is assigned.
  • At least one of the threshold values is defined as a function of a variable value at which one of the intersection points of at least two of these probability density functions occurs such that the threshold value corresponds to the variable value for this intersection point.
  • the above-mentioned segmentation criterion can thus be defined in a simple manner and used efficiently without a great deal of computational effort in order to allocate the individual points to a segment in each case.
  • the definition of the threshold value(s) as a function of the point(s) of intersection of the probability density function is particularly advantageous with regard to the goal of an assignment that is as reliable as possible (with few or no errors). Namely, if the probability density functions for the linear combination are determined by the approximation in such a way that they each approximate the respective frequency distribution of the size for a specific object well, then their integral over a specific value interval, in which to the associated value for the size lies at a certain point, with a respective one Associate the probability that the point belongs to the object approximated by the respective probability density function.
  • a point is assigned to a particular segment based on its size value as a result of comparison with the threshold, this means that it has a higher probability of belonging to the object associated with this segment than to the other object whose associated segment is determined by means of of the threshold is separated from the associated segment.
  • At least one of the m quantities for each of the points in the point cloud indicates a position of this point along this spatial direction, projected onto a selected fixed spatial direction.
  • This can be used, for example, to achieve segmentation of the image or point cloud in a two- or three-dimensional point cloud (M e ⁇ 2;3 ⁇ ) with depth dimension z on the basis of the depth information given by the point positions, in particular also in the sense a foreground/background segmentation.
  • the spatial direction can in particular correspond to the direction of a coordinate axis of a coordinate system used to define the positions of the points in the M-dimensional space.
  • the fixed spatial direction is selected to be orthogonal to a first principal component resulting from a principal component analysis applied to the point cloud. This is particularly advantageous for the detection of objects that are to be separated from the background or other objects with regard to a spatial direction that does not coincide with the direction of the first principal component, preferably even, at least essentially, is perpendicular thereto. Since the first principal component from a principal component analysis represents the dominant component for objects that are not spherically symmetric, it is consequently particularly easy to separate those objects whose dominant component runs at least largely transversely to the fixed spatial direction under consideration. If, for example, the selected fixed spatial direction corresponds to the depth direction (e.g.
  • the least dominant of the main components is thus selected as the fixed spatial direction, so that objects can be recognized or separated particularly well whose more dominant first or second main components are transverse, in particular orthogonal, to the fixed spatial direction.
  • the method further includes: filtering the image such that, after filtering, it only contains those points of the point cloud that have been assigned to one of the segments that have each been identified as representing a respective recognized object.
  • a filter function can be implemented in particular, which has the effect that only the object or objects of interest is recognized or identified, while other objects or the image background are at least largely ignored (except for those points that may have been mistakenly assigned to the object or objects). assigned to the remaining objects of interest).
  • the image can be filtered in such a way that, after filtering, it only contains those points of the point cloud that have been assigned exactly to a specific selected one of those segments that has been identified as representing an assigned recognized object.
  • a result can thus be achieved in which at most or in particular only exactly one single object is identified.
  • the size for each of the points of the point cloud indicates a position of this point along this spatial direction projected onto a selected fixed spatial direction
  • that segment is selected from the set of segments identified as representing a respective recognized object , whose assigned points according to their positions projected onto the selected fixed spatial direction viewed in the viewing direction along this spatial direction, viewed on average, are closer than the points assigned to any other of the identified segments.
  • This can be advantageously used in particular for the purpose of foreground/background segmentation if only one (or the) foremost object is to be recognized as the foreground.
  • m>1 applies and at least one of the m quantities indicates a temperature value or a color value for each of the points of the point cloud.
  • Another of the m quantities can relate in particular to the position of the respective point.
  • a particularly reliable, ie selective, segmentation can be achieved if the object(s) to be identified typically have a surface temperature that deviates from their ambient temperature, as is usually the case with living objects, in particular people or animals.
  • output data is generated (and preferably output, in particular via an interface) that represents the result of the assignment of the points to segments or the identification of at least one recognized object in one or more of the following ways: (i) the output data represent, for at least one of the objects, an image of this object based on one or more, in particular all, of those points in the point cloud which have been assigned to the segment belonging to this object; (ii) the output data represents information indicating how many different objects were recognized by the segment assignment of the points in the image; (iii) the output data represent information which indicates to which respective segment or object the points were assigned in each case; (iv) the output data represent information which, for at least a subset of the points, specifies the respective function value of one or more of the probability density functions at the point which is determined by the values of the m quantities assigned to the point.
  • the image can be determined in particular by a specific point from the set of points assigned to the segment or as a specific, in particular calculated point depending on these points, for example as the center point of the distribution of the points in the set.
  • the image can in particular also be defined as a spatial area or body spanned by the points of the set.
  • the associated (respective) probability density functions each have a course in which the function value increases as a function of the value of the variable up to a maximum and then falls again, with the maximum is the only occurring maximum in the course of the probability density function.
  • a function profile which can be bell-shaped (symmetrical or also asymmetrical), is then particularly good for the method and in particular for approximating frequency distributions for the sampling point clouds generated by objects if the object or objects each have a convex shape.
  • At least one (in particular each) of the respective probability density functions for at least one of the m quantities can be a Gaussian function.
  • At least one of the frequency distributions is subjected to a respective smoothing process and the approximation with regard to this at least one frequency distribution takes place with respect to the corresponding frequency distribution smoothed by means of the smoothing process.
  • the quality of the approximation and thus the quality and reliability of the recognition or separation of objects represented by the point cloud based thereon can be further increased.
  • a gesture recognition process is performed to recognize a gesture of a person represented in the image by means of the point cloud. This can be done in particular in the context of an automotive application, in particular in connection with a gesture recognition with regard to gestures performed by an occupant of a vehicle to control a functionality of the vehicle.
  • a second aspect of the invention relates to a system for data processing, having at least one processor which is configured in such a way that it executes the method according to the first aspect of the invention.
  • the system can be a computer or a control unit for another or higher-level system, such as for a vehicle or for a production machine or line.
  • a third aspect of the invention relates to a computer program with instructions which, when executed on a system according to the second aspect, cause the latter to carry out the method according to the first aspect.
  • the computer program can in particular be stored on a non-volatile data medium.
  • a non-volatile data medium This is preferably a data carrier in the form of an optical data carrier or a flash memory module.
  • the computer program can be present as a file on a data processing unit, in particular on a server, and can be downloaded via a data connection, for example the Internet or a dedicated data connection, such as a proprietary or local network.
  • the computer program can have a plurality of interacting individual program modules.
  • the system according to the second aspect can accordingly have a program memory in which the computer program is stored.
  • the system can also be set up to access a computer program available externally, for example on one or more servers or other data processing units, via a communication connection, in particular in order to exchange data with it that are used during the course of the method or computer program or outputs of the computer program represent.
  • 1 shows schematically various exemplary scenes, each with an object arrangement of two objects to be separated from one another, and in each case a sectional image of a corresponding point cloud detected by sensors by scanning the scene;
  • FIG. 1 to illustrate an exemplary problem addressed by the invention, an overview 100 of various exemplary scenes 105a, 110a, 115a and 120a and a corresponding sectional view 105b, 110b, 1 15b or 120b through a point cloud P is shown, which was generated by scanning the respective scene using a depth image sensor, in particular a TOF camera (time of flight sensor).
  • the depth direction to which the detected depth image relates and which measures a distance from the sensor to the respective object along the depth image sensor, is selected here as the "z" direction by way of example.
  • the TOF camera is selected here as the "z" direction by way of example.
  • a point p, in the point cloud is given by its (x,y,z) coordinates, where (x,y) is a (horizontal) plane perpendicular to the sensor's line of sight, and z is the depth value, i.e. the distance from the point to the sensor.
  • Each of the scenes shows a first object Oi, which is formed by a human hand of a person, and any other object O 2 , which can be, for example, another part of the person's body or a body belonging to an interior of a vehicle.
  • the two objects Oi and O2 are laterally adjacent in a direction perpendicular to the z-direction (eg, x-direction), with a gap between them along this direction. Due to this gap, the point cloud portions corresponding to the two objects Oi and O 2 can be divided, as in shown in sectional view 105b, easily separate from one another and assign each to a separate image segment or. This assignment is essentially error-free, at least when the gap is larger than the average point spacing within the point cloud P.
  • the two objects Oi and O2 are offset from one another in the z-direction, with a gap between them in the z-direction. Due to this gap, the point cloud portions corresponding to the two objects O1 and O2, as shown in section view 110b, can also be easily separated from each other due to their clearly different depth values (z-coordinates) and each have their own image segment and thus object O1 or assign O2. This assignment is also essentially error-free, at least when the gap is larger than the average point spacing within the point cloud P.
  • the two objects O1 and O2 are offset from one another in the z-direction, separated only by a very small gap, and they overlap in the direction perpendicular to the z-direction.
  • the corresponding point cloud P in view 115b no longer allows a division of the point cloud P into point cloud portions or segments corresponding to the two objects O1 and O2 in a similarly simple and error-free manner as in scenes 105a and 110a due to a recognized gap, because the average point spacing within the point cloud P is similar in size to the gap.
  • the starting position for an object separation is even more difficult in the case of scene 120a, in which the two objects O1 and O2 overlap or touch both in the z-direction and in a direction perpendicular thereto, so that there is no gap that can be imaged by the point cloud P here more occurs and thus an object separation or segmentation with simple means, as explained for the scenes 105a and 105b, becomes unreliable or fails completely.
  • a scene 205 containing a plurality of objects is scanned by image sensors, in particular by means of a depth image sensor, such as a TOF camera, in order to obtain an image of the scene in the form of a point cloud P, as shown in view 210.
  • the image data output by the depth image sensor can, in particular, have its respective coordinate in the depth direction, here as the z-direction, for each of the points p in the point cloud P selected, and optionally represent further coordinates or additionally measured properties of the objects.
  • the resulting frequency distribution h(k) is illustrated using a histogram that represents it.
  • the set of depth values ⁇ d , ..., d n ⁇ (in this example equivalent to the set of z coordinates of the points [p L , ...,p n ⁇ ) serves as the basis for the further steps for object separation or Segmentation.
  • the range of possible depth values is divided into a sequence of sections of length y and each point Pt of the point cloud P, at least each point to be assigned to a segment, is assigned to one of the sections according to its depth value d L .
  • the histogram then indicates, for each value JE 2 , the number of those points whose depth value corresponds approximately (ie rounded down in the present example) to j /.
  • the finitely large granularity requires the aforementioned discretization, since all values of d L within the same section are assigned the same value k t for k.
  • a normalized Gaussian function is, as usual, to be understood as a function f :IR >— > H, which can be represented using the following formula, where the mean p of the distribution, the standard deviation a and the normalization factor c are each parameters of the function f (the notation "f" and "f" are used here synonymously, the same applies to different spellings of other symbols) and with regard to the method 200 z is selected as the independent variable:
  • Gaussian functions for the approximation are advantageous in several respects.
  • the Gaussian functions f q (z) are determined by means of the approximation, a segment of the image or the point cloud P represented by them can be defined by each of these Gaussian functions. Then, for each point p L e P , the probability that that point p L belongs to a respective particular segment can be interpreted such that this probability is proportional to fq(di).
  • the associated function value fi (di) indicates the probability that this point p L belongs to a first segment of the image, and accordingly for each point p t e P the associated function value f2 ( di) indicates the probability that this point Pt belongs to a second segment of the image different from the first segment.
  • each point p t is unambiguously assigned to that segment q whose function value f q (dj) for this point is the highest among the various function values for this point.
  • one or, in this case, two objects Oi and O2 can now be identified by assigning all points of a respective segment to exactly one of these objects O1 or O2.
  • the respective segment is thus determined as a representative of the respective associated object.
  • the choice of the one-dimensional variable can influence the resulting one, particularly if it corresponds to a position along a specific direction (here the z-direction, for example). Frequency distribution, thus on the functions determined from it by approximation and finally also on the quality of the segment assignment and object identification.
  • the z-direction is selected such that it runs orthogonally to a main extension direction, represented by direction vector A, of a person's hand to be identified as object O1 within the scope of the method.
  • direction vector A a main extension direction
  • the situation shown in view 310 results that the frequency distribution is good even using a single Gaussian function can be approximated, which in turn leads to a simple and very reliable and accurate identification of the object Oi.
  • the z-direction is selected such that it is no longer orthogonal, but rather at a smaller angle to the main extension direction represented by the direction vector A of the object shown and as part of the method Oi to be identified hand of a person runs.
  • the situation shown in view 320 results here that the frequency distribution can only be well approximated using a linear combination of several Gaussian functions, which in turn leads to a more difficult and possibly less reliable or less precise identification of the object Oi.
  • the method 200 can in particular provide that the one-dimensional direction is selected on the basis of the result of a principal component analysis in such a way that a fixed spatial direction is selected for the one-dimensional quantity such that it runs orthogonally to a first principal component, which consists of a point cloud applied principal component analysis results.
  • the least dominant main component (here along the z-direction) is selected, which usually optimizes the probability that the most dominant main component is at least predominantly perpendicular to it and thus to the scanning direction (here z-direction) and therefore a more dem scenario approximated to the first scenario with optimized segment allocation and object allocation.
  • Diagram 400 relates to an extension of the method, in particular also of method 200, to the case m>1.
  • each function, especially Gaussian may represent only one object category (i.e. a set of multiple objects that is not further discriminated by the chosen feature) and not necessarily exactly a single object.
  • One approach to improving the method with regard to its selectivity includes adding at least one additional one-dimensional variable so that m>1 applies.
  • a local temperature value T recorded for the respective point can also be used as a second variable and thus as an additional basis for the assignment.
  • the hand has a higher (surface) temperature than the background and a classification of the points pi according to their respective local temperature value Ti according to a second frequency distribution h'(k'(T) related to the temperature as an independent variable ) or h'(T) for short, which in turn can be approximated by a linear function of distribution density functions gi in accordance with method 200, only this time related to the temperature instead of the z-coordinate.
  • the size z enables the point cloud to be subdivided into the categories of near object and distant object or image background.
  • the thermal quantity (temperature) T can divide the points into the categories "warm objects" and "cold objects".
  • the image background B can optionally also be viewed as a distant object.
  • P ⁇ p x , ... , p n ⁇ be a point cloud generated by the sensory scanning of the scene, with each point p L being assigned a depth value z and a measured local temperature value T at the location of the measured position of the respective point Pt becomes.
  • equation (5) an approximation according to equation (5) is carried out for the depth z of the points, initially considered as a single variable, in order to determine a linear combination of functions f q (z) which approximates the depth value distribution of the points.
  • Each of the functions f q (z) again represents a depth segment.
  • the product for the selected point p t the combination /i(Pi) ' ⁇ (Pr) is largest among all combinations, so that the concrete point pt is assigned to the combined segment (1;2). becomes, which here corresponds to the closest and at the same time warmest object.
  • the points of this combined segment can then be identified as points of an object to be recognized, here the hand Oi.
  • the method according to the invention can be used in its various variants for a wide variety of applications.
  • Such applications include, in particular, the separation of images of different body parts of a person, of different people or of one or more people on the one hand and one or more other objects on the other hand, each from one another or from a background.
  • the method can be used to separate one or more body parts of a person in an image captured by sensors, in order then, depending on the result of such a separation or segmentation and a subsequent identification of the body parts as objects, to carry out gesture recognition with regard to any of the perform gestures performed by the person.

Abstract

Procédé de reconnaissance d'un ou de plusieurs objets, qui sont représentés dans une image au moyen d'un nuage de points à M dimensions, M > 1, composé d'une pluralité de n points, consistant à : déterminer, pour chacun d'un nombre m, m > 0, des variables unidimensionnelles spécifiques, une valeur associée de la variable pour chacun des points sur la base de la position ou des propriétés du point ; déterminer, pour chacune des variables, une distribution de fréquence par rapport aux valeurs respectives de ladite variable qui ont été déterminées pour les différents points ; estimer chacune des distributions de fréquence au moyen d'une combinaison linéaire d'un nombre fini de fonctions de densité de probabilité unidimensionnelle associées à la variable en question ; segmenter l'image de telle sorte que, dans le cas de m = 1, chacune des fonctions de densité de probabilité et, dans le cas de m > 1, chaque produit de m fonctions de densité de probabilité, l'une des fonctions de densité de probabilité associée par variable représentée dans le produit, est attribuée de manière unique à un segment de l'image ; attribuer chaque point du nuage de points au segment, la fonction de densité de probabilité associée à celui-ci, dans le cas de m = 1, ou le produit associé à celui-ci, dans le cas de m > 1, a, à l'emplacement qui est déterminé par les valeurs des m variables qui sont attribuées au point, la plus grande valeur de fonction parmi les fonctions de densité de probabilité ou la plus grande valeur de produit parmi les produits ; et identifier, comme représentatif d'un objet reconnu associé, au moins l'un des segments auquel a été attribué au moins un nombre minimum prédéfini de points. Un dispositif correspondant et un programme informatique sont conçus pour mettre en œuvre le procédé.
EP21843940.4A 2021-01-13 2021-12-21 Procédé et système de reconnaissance d'objets représentés dans une image au moyen d'un nuage de points Pending EP4278329A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE102021100512.4A DE102021100512A1 (de) 2021-01-13 2021-01-13 Verfahren und system zum erkennen von in einem bild anhand einer punktwolke repräsentierten objekten
PCT/EP2021/086957 WO2022152522A1 (fr) 2021-01-13 2021-12-21 Procédé et système de reconnaissance d'objets représentés dans une image au moyen d'un nuage de points

Publications (1)

Publication Number Publication Date
EP4278329A1 true EP4278329A1 (fr) 2023-11-22

Family

ID=80112348

Family Applications (1)

Application Number Title Priority Date Filing Date
EP21843940.4A Pending EP4278329A1 (fr) 2021-01-13 2021-12-21 Procédé et système de reconnaissance d'objets représentés dans une image au moyen d'un nuage de points

Country Status (5)

Country Link
US (1) US20240144483A1 (fr)
EP (1) EP4278329A1 (fr)
CN (1) CN116888637A (fr)
DE (1) DE102021100512A1 (fr)
WO (1) WO2022152522A1 (fr)

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10593042B1 (en) * 2017-04-11 2020-03-17 Zoox, Inc. Perspective conversion for multi-dimensional data analysis

Also Published As

Publication number Publication date
WO2022152522A1 (fr) 2022-07-21
CN116888637A (zh) 2023-10-13
DE102021100512A1 (de) 2022-07-14
US20240144483A1 (en) 2024-05-02

Similar Documents

Publication Publication Date Title
DE112014003563B4 (de) Vorrichtung und Verfahren zur Gestenbestimmung, Gestenbedienungsvorrichtung, Programm und Aufzeichnungsmedium
DE102015121339B4 (de) Systeme und verfahren zum ermitteln eines zustands einer fahrbahn
DE102014209137B4 (de) Verfahren und Vorrichtung zur Kalibrierung eines Kamerasystems eines Kraftfahrzeugs
DE102018116111A1 (de) Ein einheitliches tiefes faltendes neuronales Netzwerk für die Abschätzung von Freiraum, die Abschätzung der Objekterkennung und die der Objektstellung
DE202017007675U1 (de) Computerprogrammprodukt mit einem Computerprogramm zur Verarbeitung von visuellen Daten einer Straßenoberfläche
DE102017203276B4 (de) Verfahren und Vorrichtung zur Ermittlung einer Trajektorie in Off-road-Szenarien
DE102015207676A1 (de) Verfahren und Vorrichtung zur Hinderniserkennung auf Basis einer monokularen Kamera
DE102019100575A1 (de) Verfahren und system zum erzeugen eines bereichsbildes unter verwendung von daten mit geringer tiefe
EP1298454A2 (fr) Procédé de reconnaissance et de suivi d'objets
DE102017218366A1 (de) Verfahren und system zur fussgängererfassung in einem fahrzeug
EP1531342B1 (fr) Procédé de détection des piétons
DE102015208139A1 (de) Fahrzeuginsassenerfassung mittels des Abstands zwischen Beifahrer- und Fahrermerkmalen
WO2019201565A1 (fr) Procédé, dispositif et support d'enregistrement lisible par ordinateur comprenant des instructions pour le traitement de données de capteur
DE102018132805A1 (de) Verfahren für eine verbesserte Objekterfassung
DE102008015535B4 (de) Verfahren zur Bildverarbeitung von Stereobildern
DE102018121866A1 (de) Verfahren zur Tiefenabschätzung von zweidimensionalen Sensordaten
EP3663881B1 (fr) Procédé de commande d'un véhicule autonome en fonction des vecteurs de mouvement estimés
DE10145608B4 (de) Modellbasierte Objektklassifikation und Zielerkennung
EP4278329A1 (fr) Procédé et système de reconnaissance d'objets représentés dans une image au moyen d'un nuage de points
DE102019101634A1 (de) Werkstückbilderzeugungsvorrichtung
DE102018121317A1 (de) Verfahren und Vorrichtung zur Schätzung einer durch eine Freiraumgeste vermittelten Richtungsinformation zur Bestimmung einer Benutzereingabe an einer Mensch-Maschine-Schnittstelle
EP3663800B1 (fr) Procédé de détection d'objet à l'aide d'une camera tridimensionnelle
DE102020208080A1 (de) Erkennung von Objekten in Bildern unter Äquivarianz oder Invarianz gegenüber der Objektgröße
DE102019126074A1 (de) Analyse einer Umgebung eines aktiven optischen Sensorsystems
DE102018130745A1 (de) Verfahren zur Steuerung eines autonomen Fahrzeugs

Legal Events

Date Code Title Description
STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: UNKNOWN

STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: THE INTERNATIONAL PUBLICATION HAS BEEN MADE

PUAI Public reference made under article 153(3) epc to a published international application that has entered the european phase

Free format text: ORIGINAL CODE: 0009012

STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: REQUEST FOR EXAMINATION WAS MADE

17P Request for examination filed

Effective date: 20230706

AK Designated contracting states

Kind code of ref document: A1

Designated state(s): AL AT BE BG CH CY CZ DE DK EE ES FI FR GB GR HR HU IE IS IT LI LT LU LV MC MK MT NL NO PL PT RO RS SE SI SK SM TR

DAV Request for validation of the european patent (deleted)
DAX Request for extension of the european patent (deleted)