Patent Publication Number: US-2022237808-A1

Title: Motion analysis system and motion tracking system comprising same of moved or moving objects that are thermally distinct from their surroundings

Description:
FIELD OF THE INVENTION 
     The present invention relates to a motion analysis system and to a motion tracking system comprising same of moved or moving objects that are thermally distinct from their surroundings. 
     BACKGROUND OF THE INVENTION 
     The demand for motion analysis and/or tracking systems of moved or moving objects is widespread and exists in a great variety of fields. Primarily, motion analyses are performed on living objects such as people to improve biomechanics in medicine or sport and to uncover weak points in a motion sequence. A comparable objective is pursued in industrial objects with the analysis of the movement sequences of robot arms or such grippers. The basis of any motion analysis is here the reliable capturing in particular of distance and angle/orientation data, if possible in real time. 
     Marker-Based Systems 
     In many applications, the object to be analyzed is provided here with a plurality of marker elements. Data capturing is effected using video cameras that record the changes in position of the marker elements provided on the locomotor system of the object by way of continuous digital storing of 2D video images using at least one video image recorder and feed said recordings to a data processing system for evaluation. 
     A difficulty with these applications is to track the movement of each individual marker element in the 2D video image in real time and to automatically assign a unique identity thereto. 
     A system that is adapted in this way has become known commercially under the name Aktisys® and is thoroughly described in WO 2011/141531 A1. 
     Markerless Systems 
     In addition to marker-based systems, there is also a high demand in the field of motion analysis and tracking for flexibly usable systems that operate without the provision of additional markers on the target object. 
     To achieve this, motion analysis and/or tracking systems that aim to localize markerless objects in digital image sequences have been developed. In this respect, reference is made by way of example to U.S. Pat. No. 7,257,237 B1, WO 2008/109567 A2, and WO 2012/156141 A1. Although additionally a multiplicity of various publications on this topic exists, known markerless motion analysis and/or tracking systems differ very little in terms of their basic principle. All known systems require a form of segmentation of the video images which produces 2D pixel regions according to predefined homogeneity criteria and assigns them to the objects to be captured. 
     The known prior markerless motion analysis and/or tracking systems which have been used, however, function adequately only under laboratory conditions with stable and controlled properties of the environment and/or of the target objects. 
     Outside a controlled laboratory environment, automatic, robust and highly precise motion analysis and/or tracking is not available, however, in the current prior art. Stable segmentation in any desired surroundings is here in particular prevented by
         moving objects in the background of the video images, such as spectators at a sports event or trees moving in the wind, etc.; and/or   quickly and non-homogeneously changing illumination intensities such as moved light sources (headlights), moved shadow sources (clouds) or reflections of moved surfaces (water), etc.; and/or   insufficient illumination intensities as are found in particular in caves, chambers or in the case of twilight/night recordings, etc.       

     All these items present huge challenges in particular for applications in the outdoor area. 
     Possible Uses of Interest 
     However, without solving the described problems relating to segmentation, outdoor applications remain closed for the use of motion analysis and/or tracking systems. This applies for example to the analysis of sports competitions in the open air and the behavior analysis of animals in their natural surroundings. But applications of interest in enclosed spaces (sports competition analysis, safety technology, animal research) must also overcome some of the described obstacles when the environment of an object to be analyzed and/or tracked cannot be adapted in a dedicated fashion, as in a lab, to the use of the known systems. 
     The Object on Which the Invention is Based 
     Proceeding herefrom, the present invention is based on the object of providing an improved motion analysis system and a motion tracking system comprising same of moved or moving objects that are thermally distinct from their surroundings,
         which overcomes the above prior art problems when used outside a controlled laboratory environment,   preferably without the need to provide marker elements on the object.       

     Solution According to the Invention 
     The object on which the present invention is based is achieved by a motion analysis system and a motion tracking system comprising same of moved on moving objects that are thermally distinct from their surroundings, having the features of independent patent claims  1  and  12 . 
     Advantageous configurations of the invention, which are able to be used alone or in combination with one another, are stated in the dependent claims. 
     A motion analysis system according to the invention is characterized by a camera group having at least one thermal imaging camera, a calibration unit, a synchronization unit, a segmentation unit, a reconstruction unit, a projection unit, and also an identification unit. 
     A motion tracking system according to the invention comprises such a motion analysis system and is characterized by a motion tracking unit performing a reorientation of a model of the object(s) from assigned correspondences. 
     Both systems advantageously make thermally supported segmentation independently of the environment conditions of an object that is to be analyzed and/or tracked and without the need for marker elements to be applied on the object possible. 
     New Possible Uses 
     The present invention opens up possible uses for motion analysis and/or tracking systems that are of interest here which have hitherto been closed, in particular in the fields of sports competition analysis, safety technology, and animal research: 
     Sport science currently has, among other things, the problem that movements are analyzed especially in laboratories but not where the sports movements actually take place: under competition conditions and outside. By way of markerless capturing and thermally supported segmentation as taught with the present invention, it is now possible for the first time to analyze the exact biomechanics for example of a soccer player at the moment his cruciate ligament injury happens and to track his movements. This and similar information is highly relevant for the sport, in particular in view of explaining and illustrating performance and injury issues. 
     Moreover, the data recorded by the thermal imaging camera(s) can also advantageously be used for thermographic analysis. Thermographic measurement methods offer the possibility to directly ascertain the average skin temperature during a sports activity and also to image the muscle groups that are involved in a movement. Thereby, physiological sequences of thermoregulation of the body cannot only be tracked directly, but rather it is also possible to intensively study sport-type-specific issues during physical activity. In general medicine, thermographic analysis is primarily used to detect local foci of inflammation. Since the generation and emission of heat in a healthy body are relatively symmetric, deviations from this symmetry can imply injuries and possibly illnesses. For example, diseased blood vessels, the formation of specific cancerous cells, thyroid dysfunctions, but also bone fractures or, in the case of a comparatively lower heat emission, circulatory problems can thus be detected in thermographic images. 
     More recent scientific work shows that people can also be identified on the basis of their unique gait and motion pattern. Where methods such as fingerprints and facial recognition reach their limits, motion features may be more difficult to bypass. Using a system of markerless motion analysis and/or tracking and thermally supported segmentation as taught with the present invention, it is also possible for the first time to use gait and motion patterns as features when identifying persons. 
     In the field of research of animal habitat or animal behavior patterns (zoology) and in preclinical research, the analysis and/or tracking of the motion of animals is an essential research element. Here, new medical methods are investigated particularly in the areas of Parkinson&#39;s disease, paresis and Alzheimer&#39;s. Placing markers on animals is difficult because they generally do not accept markers. Markerless capturing is here particularly in demand. However, very small end effectors and obscuration by fur frequently make clean segmentation and motion analysis and/or tracking more difficult. Furthermore, specially installed light sources influence the behavior of animals and thus falsify the data obtained. Using thermally supported segmentation as taught with the present invention, it is possible finally for the first time to bypass these problems and to offer effective markerless motion analysis and/or tracking in animals. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Additional details and further advantages of the invention will be described below with reference to preferred exemplary embodiments, to which the present invention is, however, not limited, and in connection with the attached drawings. 
       Schematically: 
         FIG. 1  shows an example of a motion analysis and/or tracking system according to the invention on the basis of a flowchart; 
         FIG. 2  shows an example of the arrangement of a first group of cameras comprising at least one thermal imaging camera and at least two video image cameras; 
         FIG. 3  shows an example of the arrangement of a second group of cameras, comprising at least two thermal imaging cameras and possibly video image cameras; 
         FIG. 4  shows an example of the arrangement of a third group of cameras, exclusively comprising two or more thermal imaging cameras; and 
         FIG. 5  shows an example of the arrangement of a fourth group of cameras, comprising a multiplicity of thermal imaging and video image cameras having an arbitrary arrangement in relation to one another. 
     
    
    
     DETAILED DESCRIPTION OF THE FIGURES In the below description of preferred embodiments of the present invention, identical reference signs designate identical or comparable components. 
       FIG. 1  shows (distributed over three sheets:  FIGS. 1   a,    1   b,    1   c ) an example of a motion analysis and/or tracking system  1  according to the invention on the basis of a flowchart. 
     Here, in the context of the present invention, “video image cameras”  21 ,  22 , . . . designate devices that record electromagnetic radiation in the visible light range (wavelength range from 400 to 780 nm) using specific detectors (video image recorder  20 ) and generate 2D (two-dimensional) video images VB from the electrical signals obtained. Said 2D video images VB are present then in the form of pixel graphics. 
     A pixel graphic is a computer-readable form of the description of an image in which the picture elements (pixels) are arranged in the form of a grid, and a pixel value is assigned to each pixel. In the case of 2D video images VB in the context of this invention, the pixel value that is assigned to the pixels is typically a specific color (a specific wavelength of visible light). 
     The term “voxel” (volumetric pixel) is correspondingly understood to mean a data element (“picture” element) in a three-dimensional grid. 
     In the context of the invention, 3D voxel models  93  of the object(s)  90  are reconstructed, that is to say designed, from segmented 2D pixel regions  91 ;  92  of recordings of at least two cameras by way of a reconstruction unit  62  according to the invention. The design (reconstruction) of a 3D voxel model  93  from segmented 2D pixel regions is occasionally also referred to as “space carving.” 
     “Thermal imaging cameras”  11 ,  12 , . . . in the context of the present invention designate devices that record electromagnetic radiation in the infrared range (“thermal radiation”; wavelength range: 780 nm to 1 mm), which is emitted in particular by living objects (people, animals). The pixel values of the 2D thermal images WB thus obtained represent temperature values which can also be advantageously used for a plausibility check of depth information in the 2D thermal image WB and/or 2D video image VB. 
     In this respect, the invention utilizes the fact that the body temperature of living objects (people, animals) is normally distinct from the temperature of inanimate objects  90  in the environment, meaning the silhouettes of living objects can be extracted well from the inanimate environment (as a background) from thermal images using what are known as threshold value methods (“thresholding”). Environment influences such as illumination conditions, color similarity between object and background, and any shadows as are disturbing for video cameras are irrelevant in thermal imaging cameras. 
     Even in the case of strong solar irradiation and the associated warming of the environment in comparison to the body temperature of living objects, it is nevertheless advantageously possible to distinguish silhouettes from the background, in particular by way of calibrating the temperature range of the image recording to a narrow region around the respective body temperature. 
     The motion analysis system according to the invention is initially characterized by a group of cameras  11 ,  12 , . . . ;  21 ,  22 , . . . having any desired arrangement in relation to one another such
         that the field of view  111 ,  121 , . . . ;  211 ,  221 , . . . of each camera  11 ,  12 , . . . ;  21 ,  22 , . . . overlaps with the field of view  111 ,  121 , . . . ;  211 ,  221 , . . . of at least one other camera  11 ,  12 , . . . ;  21 ,  22 , . . . of the group such that all fields of view  111 ,  121 ;  211 ,  211 , . . . are at least indirectly connected, and   that the camera group comprises at least a first and a second camera, the objective lenses  112 ,  122 , . . . ;  212 ,  222 , . . . of which are arranged at a distance x of at least two meters from one another and/or the optical axes  113 ,  123 , . . . ;  213 ,  223 , . . . of which are oriented at an angle a of at least 45° with respect to one another,
           wherein the first camera is a thermal imaging camera  11  for recording thermal radiation via continuous digital storage of 2D thermal images WB using at least one thermal imaging recorder  10 , and   wherein the second camera
               is a further thermal imaging camera  12     
               or
               is a video image camera  21  for recording light radiation via continuous digital storage of 2D video images VB using at least one video image recorder  20 .   
               
               

     Using a calibration unit  51 , simultaneous spatial 3D calibration of all thermal cameras  11 ,  12 , . . . and possibly video cameras  21 ,  22 , . . . with overlapping fields of view  111 ,  121 , . . . ;  211 ,  221 , . . . is ensured, for example according to the known prior art. 
     In addition, a synchronization unit  52  ensures that the recording, that is to say the continuous digital storage, of 2D thermal images WB and any 2D video images VB is effected at the same time and/or the recording time points of the 2D thermal images WB and any 2D video images VB are known. In the case of a mixed camera group comprising thermal cameras  11 ,  12 , . . . and video image cameras  21 ,  22 , . . . , it has proven expedient to preferably set the frequency of the video cameras  21 ,  22 , . . . to an integer multiple of the frequency of the thermal camera  11 ,  12 , . . . . The recording time points can be controlled for example by an external trigger signal. 
     The invention furthermore provides a segmentation unit  61  which segments, that is to say determines, associated 2D pixel regions  91 ;  92  of the object(s)  90  in the 2D thermal images WB and any 2D video images VB according to predefined homogeneity criteria. The term “segmentation” here refers to the creation of regions that are connected in terms of content by grouping together adjacent pixels or voxels according to predefined homogeneity criteria. According to the invention, preferably in particular image processing methods  80  such as background subtraction, edge detection, thresholding, region-based methods and the orientation on model silhouettes (calculated from MO) can be used for the segmentation. Here, it is possible to optionally apply different methods to 2D thermal images WB than to 2D video images VB, for example Bayes classifiers. “Homogeneity criteria” in the context of the invention are in particular the pixel and/or voxel values “color” and/or “temperature.” 
     Using a reconstruction unit  62  according to the invention, it is then possible to reconstruct, that is to say design, a 3D voxel model  93  of the object(s)  90  from segmented 2D pixel regions  91 ;  92 . The design (reconstruction) of a 3D voxel model  93  from segmented 2D pixel regions is occasionally also referred to as “space carving.” In the context of the present invention, the selection of the cameras  11 ,  12 , . . . ;  21 ,  22 , . . . that are in fact used for “space carving” can advantageously be effected in a manner specific to the application, that is to say flexibly—for example, all thermal cameras  11 ,  12 , . . . ;  21 ,  22 , . . . may always be used or only the thermal cameras  11 ,  12 , . . . may be used. Taking account of the fields of view  111 ,  121 , . . . ;  211 ,  221 , . . . of a plurality of spatially offset cameras  11 ,  12 , . . . ;  21 ,  22 , . . . , the objective lenses  112 ,  122 , . . . ;  212 ,  222 , . . . of which are arranged at a distance x of at least two meters from one another and/or the optical axes  113 ,  123 , . . . ;  213 ,  223 , . . . of which are oriented at an angle a of at least 45° with respect to one another, advantageously permits the reconstruction of a 3D voxel model  93 , in which each 3D voxel combines the information of a plurality of pixels recorded in different fields of view  111 ,  121 , . . . ;  211 ,  221 , . . . from synchronously available 2D thermal images WB and/or any 2D video images VB. Synchronization  52  and calibration  51  are necessary requirements for the reconstruction unit  62 . 
     The present invention is furthermore characterized by a projection unit  63 , by means of which the 3D voxel model  93 , which combines the pixels from a plurality of synchronously available 2D thermal images WB and/or any 2D video images VB, as reference for a search space SR, is projected back into the 2D thermal images WB and any 2D video images VB. Here, the back-projected pixels of the 3D voxel model  93  correspond to the fields of view  111 ,  121 , . . . ;  211 ,  221 , . . . of the respective 2D thermal images WB and/or any 2D video images VB, wherein segmentations of individual 2D thermal images WB and/or any 2D video images VB that can be difficult to segment in particular profit from good segmentation results of synchronously available 2D thermal images WB and/or any 2D video images VB from the fields of view  111 ,  121 , . . . ;  211 ,  221 , . . . of other cameras  11 ,  12 , . . . ;  21 ,  22 , . . . . 
     This has the advantage that the identification of silhouettes  94  of the obj ect(s)  90  in the individual 2D thermal images WB and/or any 2D video images VB can be limited to the search space SR thus produced. Consequently, the present invention, finally, is characterized by an identification unit  64 , by means of which silhouettes  94  of the object(s)  90  can be identified, i.e. detected, in synchronously available 2D thermal images WB and any 2D video images VB on the basis of the search space SR that is defined by the back projection. 
     The motion analysis system according to the invention advantageously makes possible thermally supported segmentation, both of the 2D thermal images and also of any 2D video images, specifically independently of the environment conditions of an object  90  that is to be analyzed and/or tracked and without the need for marker elements to be provided on the object  90 . The present invention thus opens up previously closed possible uses for motion analysis and/or tracking systems  1  that are of interest here, in particular in the fields of sports competition analysis, safety technology, and animal research. 
     If the image frequency, that is to say the number of images per unit time, of the 2D video images VB recorded using a video image camera  21 ,  22 , . . . is greater than the image frequency of the 2D thermal images WB recorded using a thermal imaging camera  11 ,  12 , . . . , a preferred configuration of the invention proposes a 2D supplementation unit  53  that supplements missing 2D thermal images WB in a manner such that a synchronous 2D thermal image WB is always present for each 2D video image VB. To this end, a keyframe interpolation device (not illustrated) has proven expedient, for example, for the data of the 2D thermal image WB in practice. 
     If the model frequency, that is to say the number of produced models per unit time, of the 3D voxel models  93  produced by the reconstruction unit  62  is lower than the image frequency of the 2D thermal images WB recorded using a thermal imaging camera  11 ,  12 , . . . and/or any 2D video images VB recorded using a video camera  21 ,  22 , . . . , a preferred configuration of the invention proposes a 3D supplementation unit  54  that supplements missing 3D voxel models  93  in a manner such that for each 2D thermal image WB and any 2D video image VB a synchronous 3D voxel model  93  is always present. Here, too, a keyframe interpolation device (not illustrated) has proven expedient, for example, for the 3D data of the voxel model  93  in practice. 
       FIG. 2  shows an example of the arrangement of a first group of cameras, comprising at least one thermal imaging camera  11 ,  12 , . . . and at least two video image cameras  21 ,  22 , . . . .  FIG. 2  shows how
         a thermal imaging camera  11  as a first camera and a video image camera  21  as a second camera are provided, the objective lenses  112 ;  212  of which are arranged at a distance x of at least two meters from one another and/or the optical axes  113 ;  213  of which are oriented at an angle a of at least 45° with respect to one another,   and how a video image camera  22  is provided as a third camera, the objective lens  222  of which is arranged immediately adjacent to the objective lens  112  of the thermal imaging camera  11  such that the optical axes  113 ;  223  of both cameras  11 ,  22  are substantially oriented parallel with respect to one another.       

     The arrangement of at least one thermal imaging camera  11  in a group of cameras comprising at least two video cameras  21 ,  22 , . . . advantageously permits at least a first plausibility check of only insufficiently segmentable 2D video images VB by the segmentation unit  61  and thus the advantageous reconstruction of 3D voxel models  93  containing fewer errors than can be found in the prior art. 
       FIG. 1  optionally shows an iterative sequence (process), in which the segmentation unit  61  and the reconstruction unit  62  are cycled through repeatedly. Here, the motion analysis and/or tracking system  1  in a further preferred configuration comprises a further segmentation unit  61 , which additionally takes into consideration limitations of the search space SR based on the results of the 3D voxel model of the preceding iteration step and adapts homogeneity criteria to the current iteration step. This advantageously increases the robustness of the system  1 ; in particular segmentations of individually poorly segmentable 2D thermal images WB and/or any 2D video images VB profit from good segmentation results in other synchronously available 2D thermal images WB and/or any 2D video images VB from the fields of view  111 ,  121 , . . . ;  211 ,  221 , . . . of other cameras  11 ,  12 , . . . ;  21 ,  22 , . . . . 
     In an alternative or cumulative configuration, the robustness of the system  1  can be increased further by a reconstruction unit  62 , which selects, in an iterative sequence, additionally the segmented 2D pixel regions  91 ,  92  used for reconstruction of the 3D voxel model  93  in dependence on the current iteration step, the type of the camera  11 ;  21 ;  31  and/or the quality criteria of the 2D pixel regions. In particular, a reconstruction unit  62  which reconstructs the 3D voxel model  93  additionally on the basis of the depth image TB of a depth image camera  31  has proven expedient. As a result, an iterative sequence of search space limitations is advantageously available, consisting of segmentation unit  61 , reconstruction unit  62 , and projection unit  63 . 
       FIG. 3  shows an example of the arrangement of a second group of cameras, comprising at least two thermal imaging cameras  11 ,  12 , . . . and possibly video image cameras  21 ,  22 , . . . .  FIG. 3  shows how, for example,
         a thermal imaging camera  11  is provided as a first camera and a thermal imaging camera  12  is provided as a second camera, the objective lenses  112 ;  122  of which are arranged at a distance x of at least two meters from one another and/or the optical axes  113 ;  123  of which are oriented at an angle a of at least 45° with respect to one another,   and how a video image camera  21  is provided as a third camera, the objective lens  212  of which is arranged directly adjacent to the objective lens  122  of the second thermal imaging camera  12  in a manner such that the optical axes  123 ;  213  of both cameras  12 ,  21  are oriented substantially parallel with respect to one another.       

       FIG. 4  shows an example of the arrangement of a third group of cameras, comprising a multiplicity of, in particular two to three, thermal imaging cameras  11 ,  12 , . . . and, in particular five to six, video imaging cameras  21 ,  22 , . . . of any desired arrangement in relation to one another. In the case of fewer cameras, the positions of the cameras would expediently be occupied in the order of the camera numbers  11 ,  12 , . . . ;  21 ,  22 , . . . or be adapted to the specific requirements of the application-specific motion analysis and/or tracking. 
     The arrangement of a group of cameras comprising at least two thermal imaging cameras  11 ,  12 , . . . —as proposed for example in  FIG. 3  or  FIG. 4 —advantageously permits a particularly reliable segmentation of 2D thermal images WB using the segmentation unit  61 . For example, as few as two thermal cameras  11 ,  12 , . . . , the optical axes  113 ,  123 , . . . of which are arranged at an appropriate angle a, allow the reconstruction of a 3D voxel model  93  purely from 2D thermal images WB. For this reason, a reconstruction unit  62  which initially reconstructs a 3D voxel model  93  of the object(s)  90  only from segmented 2D WB pixel regions  91  is preferred according to the invention. 
     The 3D WB voxel model  93  obtained in this way purely from data of the 2D thermal image WB is particularly reliable in as far as it offers a particularly robust limitation of a search space SR in the 2D video images VB of the video cameras  21 ,  22 , . . . . For this reason, a projection unit  63  which initially projects back a 3D thermal image WB voxel model  93  as a reference for a search space SR into the synchronously available 2D thermal images WB and any 2D video images VB is preferred according to the invention. 
     In a further preferred configuration, the motion analysis and/or tracking system  1  furthermore comprises an assignment unit  65  which assigns points of the identified silhouettes  94  points of previously known silhouettes  95  of a model MO of the object(s)  90  as a correspondence and/or assigns points of the previously known silhouettes  95  of a model MO of the object(s)  90  points of the identified silhouettes  94  as correspondence. The model MO advantageously represents a virtual imaged presentation of the object(s)  90 . In a typical case, it will be embodied as a kinematic chain with an associated dot grid and possibly further references on sensors. It is thus possible to project the model MO in its current orientation into the 2D thermal images WB and any 2D video images VB using the calibration unit  51  and to determine the outline, i.e. the silhouette  95 . 
     An assignment unit  65  which, optionally, additionally to the correspondences obtained from data of the silhouettes  95 , uses data in particular of further sensors  40 , any image processing units  80  and/or a depth image camera  31 ,  32 , . . . for establishing correspondences has in particular proven expedient here. These are correlated with status variables of a model MO, that is to say properties with respect to the current orientation of a model MO. As a result, an assignment unit which advantageously establishes additional correspondences by assigning further status variables of a model MO of the obj ect(s)  90  to data in particular of further sensors  40 , any image processing units  80  and/or a depth image camera  31 ,  32 , . . . is obtainable. In particular, orientation sensors (gyroscopes), acceleration sensors or active thermal markers have proven expedient. In terms of image processing units  80 , known facial recognition means, pattern recognition means or what are known as pattern matches are preferred. The depth image camera  31 ,  32 , . . . used can be any camera that permits imaging representation of distances. In this case, every pixel does not receive the color of the object  90  that can be seen, as in a video camera  21 ,  22 , . . . , or the temperature of the object, as in a thermal imaging camera  11 ,  12 , . . . , but the distance of the point of the object  90  that is visible in the corresponding pixel. Depth image cameras  31 ,  32 , . . . are available in different embodiments, such as:
         stereo cameras;   structured light; here, a light pattern, produced from light of the visible or infrared wavelength range, is projected onto the scene to be recorded, is recorded with a camera, and the depth information is calculated from the distortion of the pattern with respect to the non-distorted pattern;   time-of-flight (TOF) cameras, which infer the distance from time-of-flight measurements of the light; or   light field cameras, which determine not only the position and intensity of the incident light, but also the angle, and thus permit calculation of depth information.       

     A weighting unit  66  which weights established correspondences in accordance with fixedly predefined and/or variable parameters has also proven expedient. In particular, weighting criteria that a user can adapt possibly using parameters can be implemented. 
     In a further preferred embodiment, the motion analysis and/or tracking system  1  furthermore comprises a motion tracking unit  71 , which performs a reorientation of a model MO of the object(s)  90  from assigned correspondences. 
     Here, in particular a motion tracking unit  71  which, in an iterative procedure, carries out after each iteration, with already present correspondences and/or with correspondences which have been re-established on the basis of an updated model MO, a reorientation of a model MO of the object(s)  90  until the orientation of the model MO meets a predefined criterion has proven expedient. Such a criterion is met for example when the orientation of the model MO changes less than a predefined threshold value or when a specific number of iterations has been reached. This advantageously provides new silhouettes  95 . 
     In a further preferred embodiment, the motion analysis and/or tracking system  1  furthermore comprises a motion analysis unit  72 , which analyzes poses, in particular knee or other joint angles that are present, and/or movements of the obj ect(s)  90  from a finally available orientation of a model MO of the object(s)  90 . 
     In a further advantageous embodiment, the motion analysis and/or tracking system  1  can be supplemented by a visualization unit  73 . Here, a visualization unit  73  with which it is possible optionally to present the temperature data of the segmented 2D WB pixel regions  91  on the 3D voxel model  93  or on an, in particular finally, aligned model MO using texture mapping has proven expedient. Such visualization of the temperature data can advantageously make possible thermographic analyses in particular during the motion sequence of an object  90  to be examined. 
     Finally,  FIG. 5  shows an example of the arrangement of a fourth group of cameras, exclusively comprising two or more thermal imaging cameras  11 ,  12 ,  13 , . . . .  FIG. 5  shows how the objective lenses  112 ,  122 ,  132  of the three thermal imaging cameras  11 ,  12 ,  13 , which are illustrated by way of example, are arranged at a distance x of at least two meters from one another and/or whose optical axes  113 ,  123 ,  133  are orientated at an angle α of at least 45° with respect to one another. The arrangement of a group of cameras formed exclusively from thermal imaging cameras  11 ,  12 ,  13 , . . . advantageously permits the motion analysis and/or tracking of objects  90  even in complete darkness, which is of interest in particular for researching nocturnal animals or in crime control. 
     The motion analysis and/or tracking system  1  according to the invention advantageously permits thermally supported segmentation, both of the 2D thermal images and of any 2D video images, specifically independently of the environment conditions of an object  90  that is to be analyzed and/or tracked and without the need for marker elements to be provided on the object  90 . The present invention thus opens up previously closed possible uses for motion analysis and/or tracking systems  1  that are of interest here, in particular in the fields of sports competition analysis, safety technology, and animal research. 
     LIST OF REFERENCE SIGNS 
     
         
           1  motion analysis and/or tracking system 
           10  thermal imaging recorder 
           11 ,  12 , . . . thermal imaging cameras 
           111 ,  121 , . . . field of view of the thermal imaging camera ( 11 ,  12 , . . . ) 
           112 ,  122 , . . . objective lens of the thermal imaging camera ( 11 ,  12 , . . . ) 
           113 ,  123 , . . . optical axis (direction of view) of the thermal imaging camera ( 11 ,  12 , . . . ) 
           20  video image recorder 
           21 ,  22 , . . . video image cameras 
           211 ,  221 , . . . field of view of the video image camera ( 21 ,  22 , . . . ) 
           212 ,  222 , . . . objective lens of the video image camera ( 21 ,  22 , . . . ) 
           213 ,  223 , . . . optical axis (direction of view) of the video image camera ( 21 ,  22 , . . . ) 
           30  depth image recorder 
           31 ,  32 , . . . depth image cameras 
           311 ,  321 , . . . field of view of the depth image camera ( 31 ,  32 , . . . ) 
           312 ,  322 , . . . objective lens of the depth image camera ( 31 ,  32 , . . . ) 
           313 ,  333 , . . . optical axis (direction of view) of the depth image camera ( 31 ,  32 , . . . ) 
           40  other sensors 
           51  calibration unit 
           52  synchronization unit 
           53  2D supplementation unit, in particular keyframe interpolation device 
           54  3D supplementation unit, in particular keyframe interpolation device 
           61  segmentation unit 
           62  reconstruction unit 
           63  projection unit 
           64  identification unit 
           65  assignment unit 
           66  weighting unit 
           71  motion tracking unit 
           72  motion analysis unit 
           73  visualization unit 
           80  various image processing units 
           90  object 
           91  2D WB pixel regions(s) of the object(s) ( 90 ) 
           92  2D VB pixel regions(s) of the object(s) ( 90 ) 
           93  3D voxel model of the object(s) ( 90 ) 
           94  identified silhouettes of the object(s) ( 90 ) 
           95  (previously) known silhouettes of the model (MO) of the object(s) ( 90 ) 
         MO model of the object ( 90 ) 
         WB 2D thermal image 
         VB 2D video image 
         TB depth image 
         SR search space 
         x distance between the objective lens ( 112 ) of the first camera ( 11 ) and the objective lens ( 122 ) or ( 212 ) of the second camera ( 12 ) or ( 21 ) 
         α angle between the optical axis ( 113 ) of the first camera ( 11 ) and the optical axis ( 123 ) or ( 213 ) of the second camera ( 12 ) or ( 21 )