Patent Publication Number: US-10331974-B2

Title: Action recognition system with landmark localization on objects in images using convolutional neural networks

Description:
RELATED APPLICATION INFORMATION 
     This application claims priority to provisional application Ser. No. 62/418,894 filed on Nov. 8, 2016, incorporated herein by reference. This application is related to an application entitled “Landmark Localization On Objects In Images Using Convolutional Neural Networks”, having Ser. No. 15/709,748, and which is incorporated by reference herein in its entirety. This application is related to an application entitled “Advanced Driver-Assistance System With Landmark Localization On Objects In Images Using Convolutional Neural Networks”, having Ser. No. 15/709,814, and which is incorporated by reference herein in its entirety. This application is related to an application entitled “Surveillance System With Landmark Localization On Objects In Images Using Convolutional Neural Networks”, having Ser. No. 15/709,849, and which is incorporated by reference herein in its entirety. 
    
    
     BACKGROUND 
     Technical Field 
     The present invention relates to object parsing, and more particularly to an action recognition system with landmark localization on objects in images using convolutional neural networks. 
     Description of the Related Art 
     Parsing 3D object geometry is a critical capability for occlusion reasoning and scene understanding. However, current approaches to parsing 3D object geometry suffer from many deficiencies including, but not limited to, the lack of joint optimization for 2D and 3D keypoints, partial view ambiguity, 3D prediction errors, applicability to only low resolution images, and so forth. 
     Thus, there is a need for an improved approach to landmark localization on objects in images, particularly with respect to action recognition systems. 
     SUMMARY 
     According to an aspect of the present invention, an action recognition system is provided. The action recognition system includes an image capture device configured to capture an actual image depicting an object. The action recognition system further includes a processor. The processor is configured to render, based on a set of 3D Computer Aided Design (CAD) models, a set of synthetic images with corresponding intermediate shape concept labels. The processor is further configured to form a multi-layer Convolutional Neural Network (CNN) which jointly models multiple intermediate shape concepts, based on the rendered synthetic images. The processor is also configured to perform an intra-class appearance variation-aware and occlusion-aware 3D object parsing on the actual image by applying the CNN to the actual image to generate an image pair including a 2D geometric structure and a 3D geometric structure of the object depicted in the actual image. The processor is additionally configured to control a hardware device to perform a response action in response to an identification of an action performed by the object, wherein the identification of the action is based on the image pair. 
     According to another aspect of the present invention, a method is provided for action recognition. The method includes capturing, by an image capture device, an actual image depicting an object. The method further includes rendering, by a processor based on a set of 3D Computer Aided Design (CAD) models, a set of synthetic images with corresponding intermediate shape concept labels. The method also includes forming, by the processor, a multi-layer Convolutional Neural Network (CNN) which jointly models multiple intermediate shape concepts, based on the rendered synthetic images. The method additionally includes performing, by the processor, an intra-class appearance variation-aware and occlusion-aware 3D object parsing on the actual image by applying the CNN to the actual image to generate an image pair including a 2D geometric structure and a 3D geometric structure of the object depicted in the actual image. The method also includes controlling, by the processor, a hardware device to perform a response action in response to an identification of an action performed by the object, wherein the identification of the action is based on the image pair. 
     According to yet another aspect of the present invention, a computer program product is provided for action recognition. The computer program product includes a non-transitory computer readable storage medium having program instructions embodied therewith. The program instructions are executable by a computer to cause the computer to perform a method. The method includes capturing, by an image capture device, an actual image depicting an object. The method further includes rendering, by a processor based on a set of 3D Computer Aided Design (CAD) models, a set of synthetic images with corresponding intermediate shape concept labels. The method also includes forming, by the processor, a multi-layer Convolutional Neural Network (CNN) which jointly models multiple intermediate shape concepts, based on the rendered synthetic images. The method additionally includes performing, by the processor, an intra-class appearance variation-aware and occlusion-aware 3D object parsing on the actual image by applying the CNN to the actual image to generate an image pair including a 2D geometric structure and a 3D geometric structure of the object depicted in the actual image. The method also includes controlling, by the processor, a hardware device to perform a response action in response to an identification of an action performed by the object, wherein the identification of the action is based on the image pair. 
     These and other features and advantages will become apparent from the following detailed description of illustrative embodiments thereof, which is to be read in connection with the accompanying drawings. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
       The disclosure will provide details in the following description of preferred embodiments with reference to the following figures wherein: 
         FIG. 1  shows an exemplary system for action recognition with landmark localization on objects in images using convolutional neural networks, in accordance with an embodiment of the present invention; 
         FIG. 2  shows an exemplary Advanced Driver-Assistance System (ADAS) with landmark localization on objects in images using convolutional neural networks, in accordance with an embodiment of the present invention; 
         FIG. 3  shows an exemplary system for surveillance with landmark localization on objects in images using convolutional neural networks, in accordance with an embodiment of the present principles; 
         FIG. 4  shows an exemplary system for landmark localization on objects in images using convolutional neural networks, in accordance with an embodiment of the present invention; 
         FIG. 5  shows an exemplary processing system to which the present principles may be applied, according to an embodiment of the present principles; 
         FIGS. 6-13  show an exemplary method for landmark localization on objects in images using convolutional neural networks, in accordance with an embodiment of the present principles; 
         FIG. 14  illustrates an overall framework of the present invention, in accordance with an embodiment of the present invention; and 
         FIG. 15  illustrates an exemplary instance of deeply supervised CNN using shape concepts, in accordance with an embodiment of the present invention. 
     
    
    
     DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS 
     The present invention is directed to an action recognition system with landmark localization on objects in images using convolutional neural networks. 
     In an embodiment, a deep Convolutional Neural Network (CNN) is provided to localize object semantic parts in two-dimensional (2D) image domain and three-dimensional (3D) space given a single RGB image. The reconstruction of the 3D object structure involves building a variety of 3D-related concepts such as object pose and visibility of object parts. In an embodiment, the CNN is trained by hierarchically supervising the CNN with these aspects at different depths, in order to sequentially recover intermediate and final 2D/3D shape properties in the inverse rendering. In an embodiment, to provide diverse data with the structured labels needed in training, a number of synthetic training images are generated by rendering object CAD models under different viewpoints, lighting conditions, real image backgrounds and occlusion patterns. 
     Hence, in an embodiment, the present principles provide an approach for intra-class appearance variation-aware and occlusion-aware 3D object parsing, where “intra-class appearance variation aware” refers to being able to distinguish between appearance variations of objects in the same class for the sake of object parsing, and where “occlusion-aware 3D object parsing” refers to being able to parse an object despite the presence of an occlusion to the object. 
     Given the present invention&#39;s applicability to object parsing, it is to be appreciated that the term “object” can refer to an object in whole or a portion(s) of the object. 
     For the sake of illustration, various embodiments of the present invention are described with respect to four exemplary applications, namely (1) 3D object parsing, (2) Advanced Driver-Assistance System (ADAS), (3) surveillance, and (4) action recognition. Of course, the present invention can be used for other applications, as readily appreciated by one of ordinary skill in the art, while maintaining the spirit of the present invention. 
     In an embodiment, the input to an application, such as any of the aforementioned four applications or some other application, is an image that includes one or multiple objects (cars, humans, etc.). However, the input to the CNN is an image that contains only one object, which is either fully visible or partially occluded. Therefore, for the four applications, we will first run an object detection method on the input image to get a bounding box for each object, which becomes the input image for the CNN. In other words, we will run the CNN multiple times (one time for one bounding box cropped from the original input image). Similarly, the rendered synthetic images for training the CNN also contain only one object, which is either fully visible or partially occluded. In cases of occlusion rendering, we put two objects nearby, project them both to the image plane, and crop one bounding box for each object. 
     Various exemplary applications will now be described to which the present invention can be applied. 
       FIG. 1  shows an exemplary system  100  for action recognition with landmark localization on objects in images using convolutional neural networks, in accordance with an embodiment of the present invention. 
     The system  100  includes a camera system  110 . While a single camera system  110  is shown in  FIG. 1  for the sakes of illustration and brevity, it is to be appreciated that multiple camera systems can be also used, while maintaining the spirit of the present invention. 
     In the embodiment of  FIG. 1 , the camera system  110  is mounted on a mounting entity  160 . For the sake of illustration, the mounting entity  160  is a pole. While a pole  160  is shown (as a mounting entity) for the sake of illustration, any other mounting entity can be used, as readily appreciated by one of ordinary skill in the art given the teachings of the present invention provided herein, while maintaining the spirit of the present invention. For example, the camera system  110  can be mounted in or on any of the following: a building; and so forth. The preceding examples are merely illustrative. 
     The camera system  110  can be a wireless camera system having its own antenna(s) or can use one or more antennas included on the pole  160  (or other mounting entity (e.g., building, drone, etc.) to which the camera system  110  is mounted or proximate). 
     The system  100  further includes a server  120  configured to perform action recognition based on landmark localization on objects  199  including 3D object parsing (hereinafter “landmark localization” in short) in accordance with the present invention. Such action recognition can be with respect to a secured object such as, for example, a facility  177  or a portion of the facility  177 . The facility can be a secured facility or a non-secured facility. In the example of  FIG. 1 , the facility is a secured facility implemented as an airport. In an embodiment, a portion of the facility can be involved such as a departure terminal, etc. Of course, other facilities (e.g., mass transit hub, sports complex, etc.) and/or portions thereof and/or other portions of the aforementioned facility can also be monitored with respect to action recognition based on landmark localization in accordance with the present invention. The action recognition can involve detecting the presence of objects, identifying the locations of the objects, parsing the objects into object portions, identifying particular actions performed by the objects and/or object portions and/or particular actions performed by one or more persons using the objects and/or object portions, and/or performing one or more actions (e.g., in response to any of: the detecting of the objects; the identifying of the object locations; the parsing of the objects; the recognition of an action performed by the objects and/or object portions and/or the output 2D image and/or output 3D image described further herein below). The server  120  can be located remote from, or proximate to, the camera system  110 . The server  120  can include a processor  121 , a memory  122 , and a wireless transceiver  123 . The processor  121  and the memory  122  of the remote server  120  can be configured to perform action recognition based on images received from the camera system  110  by (the wireless transceiver  123  of) the remote server  120 . 
     In this way, based on an input image depicting an object(s), the server  120  can output and/or otherwise provide an image  199 A of a 2D geometric structure of the object(s)  199  (or a portion(s) thereof) and/or an image  199 B of a 3D geometric structure (e.g., a skeletonized structure) of the object(s)  199  (or a portion(s) thereof) for any of a myriad of possible action recognition application uses. In an embodiment, the images  199 A and  199 B can be provided on a display  124  coupled to the server  120 . In an embodiment, possible action recognition application uses can involve one or more actions performed responsive to the list, as readily appreciated by one of ordinary skill in the art. For example, an alert (local and/or remote) can be provided, one or more doors and/or windows can be closed and locked to secure the person within a specific area or to keep the person from (out of) that specific area, a person containment procedure can be automatically performed, and so forth. 
     Accordingly, some exemplary suitable environments to which the present invention can be applied can include any environments where action recognition can prove useful such as mass transit hubs, border crossings, subways, transportation hubs, airports, ship ports, sports facilities, and so forth. It is to be appreciated that the preceding environments are merely illustrative and, thus, other environments can also be used, while maintaining the spirit of the present invention. 
     Any action type of interest can be recognized, depending upon the implementation. For example, the action may include, but is not limited to, one or more of the following: an intruder running up to a premises or an object; a projectile approaching the premises or the object; a sporting action; a prohibited action (e.g., holding a weapon in a place where weapons are prohibited except for, e.g., law enforcement, etc.); a potentially dangerous action; and so forth. It is to be appreciated that the preceding actions are merely illustrative. 
       FIG. 2  shows an exemplary Advanced Driver-Assistance System (ADAS)  200  with landmark localization on objects in images using convolutional neural networks, in accordance with an embodiment of the present invention. 
     The ADAS  200  is used in an environment  201  wherein a user  288  is located in a scene with multiple objects  299 , each having their own locations and trajectories. The user  288  is operating a vehicle  272  (e.g., a car, a truck, a motorcycle, etc.). 
     The ADAS  200  includes a camera system  210 . While a single camera system  210  is shown in  FIG. 2  for the sakes of illustration and brevity, it is to be appreciated that multiple camera systems can be also used, while maintaining the spirit of the present invention. 
     The ADAS  200  further includes a server  220  configured to perform advanced driver assistance functions (hereinafter “driver assistance functions” in short) based on landmark localization on objects  299  including 3D object parsing (hereinafter “landmark localization” in short) in accordance with the present invention. The server  220  can include a processor  221 , a memory  222 , and a wireless transceiver  223 . The processor  221  and the memory  222  of the remote server  220  can be configured to perform driver assistance functions based on images received from the camera system  210  by (the wireless transceiver  223  of) the remote server  220 . In this way, based on an input image depicting an object(s), the server  220  can output and/or otherwise provide an image  299 A of a 2D geometric structure of the object(s)  299  and an image  299 B of a 3D geometric structure (e.g., a skeletonized structure) of the object(s)  299  to the user  288  and/or the vehicle  272  in order for corrective action to be taken by the user  288  and/or the vehicle  272 . Possible corrective actions can involve one or more actions performed responsive to the output images or data relating thereto, as readily appreciated by one of ordinary skill in the art. In an embodiment, the images  299 A and  299 B can be provided on a display  224  coupled to the server  220 . The display  224  may be a heads-up display projected onto the windshield of the vehicle  272 . Of course, other implementations for presenting the data depicted in the images to a user can also be used, while maintaining the spirit of the present invention. 
     The ADAS  200  can interface with the user through one or more systems of the vehicle  272  that the user is operating. For example, the ADAS  200  can provide the user information (e.g., detected objects  299  (or portions of the detected objects  299 ), their locations, suggested actions, etc.) through a system (e.g., a display system, a speaker system, and/or some other system) of the vehicle  272 . Moreover, the ADAS  200  can interface with the vehicle  272  itself (e.g., through one or more systems of the vehicle  272  including, but not limited to, a steering system, a braking system, an acceleration system, etc.) in order to control the vehicle or cause the vehicle  272  to perform one or more actions. In this way, the user or the vehicle  272  itself can navigate around these objects  299  to avoid potential collisions there between. 
       FIG. 3  shows an exemplary system  300  for surveillance with landmark localization on objects in images using convolutional neural networks, in accordance with an embodiment of the present principles. 
     The system  300  includes a camera system  310 . While a single camera system  310  is shown in  FIG. 3  for the sakes of illustration and brevity, it is to be appreciated that multiple camera systems can be also used, while maintaining the spirit of the present invention. 
     In the embodiment of  FIG. 3 , the camera system  310  is mounted on a mounting entity  360 . For the sake of illustration, the mounting entity  360  is a pole. While a pole  360  is shown (as a mounting entity) for the sake of illustration, any other mounting entity can be used, as readily appreciated by one of ordinary skill in the art given the teachings of the present invention provided herein, while maintaining the spirit of the present invention. For example, the camera system  310  can be mounted in or on any of the following: a building; and so forth. The preceding examples are merely illustrative. 
     The camera system  310  can be a wireless camera system having its own antenna(s) or can use one or more antennas included on the pole  360  (or other mounting entity (e.g., building, drone, etc.) to which the camera system  310  is mounted or proximate). 
     The system  300  further includes a server  320  configured to perform surveillance based on landmark localization on objects  399  including 3D object parsing (hereinafter “landmark localization” in short) in accordance with the present invention. Such surveillance can be with respect to a secured object such as, for example, a secured facility  377 . In the example of  FIG. 3 , the secured facility is an airport. Of course, other secured facilities can also be surveilled in accordance with the present invention. The surveillance can involve detecting the presence of objects, identifying the locations of the objects, parsing the objects into portions, identifying particular actions performed by the objects or the portions thereof, and/or performing one or more actions (e.g., in response to any of: the detecting of the objects; the identifying of the object locations; the parsing of the objects; and/or the output 2D image and/or output 3D image described further herein below). The server  320  can be located remote from, or proximate to, the camera system  310 . The server  320  can include a processor  321 , a memory  322 , and a wireless transceiver  323 . The processor  321  and the memory  322  of the remote server  320  can be configured to perform surveillance based on images received from the camera system  310  by (the wireless transceiver  323  of) the remote server  320 . 
     In this way, based on an input image depicting an object(s), the server  320  can output and/or otherwise provide an image  399 A of a 2D geometric structure of the object(s)  399  (or a portion(s) thereof) and/or an image  399 B of a 3D geometric structure (e.g., a skeletonized structure) of the object(s)  399  (or a portion(s) thereof) for any of a myriad of possible surveillance application uses. In an embodiment, the images  399 A and  399 B can be provided on a display  324  coupled to the server  320 . In an embodiment, possible surveillance application uses can involve one or more actions performed responsive to the output images or data relating thereto, as readily appreciated by one of ordinary skill in the art. For example, an alert (local and/or remote) can be provided, one or more doors and/or windows can be closed and locked to secure the person within a specific area or to keep the person from (out of) that specific area, a person containment procedure can be automatically performed, and so forth. 
     Accordingly, some exemplary suitable environments to which the present invention can be applied can include any environments where surveillance can prove useful such as mass transit hubs, border crossings, subways, transportation hubs, airports, ship ports, and so forth. It is to be appreciated that the preceding environments are merely illustrative and, thus, other environments can also be used, while maintaining the spirit of the present invention. 
       FIG. 4  shows an exemplary system  400  for landmark localization on objects in images using convolutional neural networks, in accordance with an embodiment of the present invention. 
     The system  400  includes a camera system  410 . While a single camera system  410  is shown in  FIG. 4  for the sakes of illustration and brevity, it is to be appreciated that multiple camera systems can be also used, while maintaining the spirit of the present invention. 
     In the embodiment of  FIG. 4 , the camera system  410  is mounted on a mounting entity  460 . For the sake of illustration, the mounting entity  460  is a pole. While a pole  460  is shown (as a mounting entity) for the sake of illustration, any other mounting entity can be used, as readily appreciated by one of ordinary skill in the art given the teachings of the present invention provided herein, while maintaining the spirit of the present invention. For example, the camera system  410  can be mounted in or on any of the following: a building; a drone; a vehicle; and so forth. The preceding examples are merely illustrative. 
     The camera system  410  can be a wireless camera system having its own antenna(s) or can use one or more antennas included on the pole  460  (or other mounting entity (e.g., building, drone, vehicle, etc.) to which the camera system  410  is mounted or proximate). 
     The system  400  further includes a server  420  configured to perform landmark localization on objects  499  including 3D object parsing (hereinafter “landmark localization” in short) in accordance with the present invention. The landmark localization can involve detecting the presence of objects, identifying the locations of the objects, parsing the objects into portions, identifying particular actions performed by the objects or the portions thereof, and/or performing one or more actions (e.g., in response to any of: the detecting of the objects; the identifying of the object locations; the parsing of the objects; and/or the output 2D image and/or output 3D image described further herein below). The server  420  can be located remote from, or proximate to, the camera system  410 . The server  420  can include, e.g., a processor  421 , a memory  422 , and a wireless transceiver  423 . The processor  421  and the memory  422  of the remove server  420  can be configured to perform landmark localization based on images received from the camera system  410  by (the wireless transceiver  423  of) the remote server  420 . 
     In this way, based on an input image depicting an object(s), the server  420  can output and/or otherwise provide an image  499 A of a 2D geometric structure of the object(s)  499  (or a portion(s) thereof) and/or an image  499 B of a 3D geometric structure (e.g., a skeletonized structure) of the object(s)  499  (or a portion(s) thereof) for any of a myriad of possible application uses. Such application uses can involve one or more actions performed responsive to the output images or data relating thereto, as readily appreciated by one of ordinary skill in the art. In an embodiment, the images  499 A and  499 B can be provided on a display  424  coupled to the server  420 . 
     Accordingly, some exemplary suitable environments to which the present invention can be applied can include any environments where object detection can be used. For example, exemplary suitable environments include, but are not limited to, a driving environment, an environment to be surveilled, a sports or other event, a battle field, a riot scenario, a picketing scenario, video analysis, object reconstruction, and so forth. 
       FIG. 5  shows an exemplary processing system  500  to which the present principles may be applied, according to an embodiment of the present principles. In an embodiment, the server  120  of  FIG. 1  and/or the server  220  of  FIG. 2  and/or the server  320  of  FIG. 3  and/or the server  420  of  FIG. 4  can be implemented, at least in part, by processing system  500 . 
     The processing system  500  includes at least one processor (CPU)  504  operatively coupled to other components via a system bus  502 . A cache  506 , a Read Only Memory (ROM)  508 , a Random Access Memory (RAM)  510 , an input/output (I/O) adapter  520 , a sound adapter  530 , a network adapter  540 , a user interface adapter  550 , and a display adapter  560 , are operatively coupled to the system bus  502 . 
     A first storage device  522  and a second storage device  524  are operatively coupled to system bus  502  by the I/O adapter  520 . The storage devices  522  and  524  can be any of a disk storage device (e.g., a magnetic or optical disk storage device), a solid state magnetic device, and so forth. The storage devices  522  and  524  can be the same type of storage device or different types of storage devices. 
     A speaker  532  is operatively coupled to system bus  502  by the sound adapter  530 . A transceiver  542  is operatively coupled to system bus  502  by network adapter  540 . A display device  562  is operatively coupled to system bus  502  by display adapter  560 . 
     A first user input device  552 , a second user input device  554 , and a third user input device  556  are operatively coupled to system bus  502  by user interface adapter  550 . The user input devices  552 ,  554 , and  556  can be any of a keyboard, a mouse, a keypad, an image capture device, a motion sensing device, a microphone, a device incorporating the functionality of at least two of the preceding devices, and so forth. Of course, other types of input devices can also be used, while maintaining the spirit of the present principles. The user input devices  552 ,  554 , and  556  can be the same type of user input device or different types of user input devices. The user input devices  552 ,  554 , and  556  are used to input and output information to and from system  500 . 
     Of course, the processing system  500  may also include other elements (not shown), as readily contemplated by one of skill in the art, as well as omit certain elements. For example, various other input devices and/or output devices can be included in processing system  500 , depending upon the particular implementation of the same, as readily understood by one of ordinary skill in the art. For example, various types of wireless and/or wired input and/or output devices can be used. Moreover, additional processors, controllers, memories, and so forth, in various configurations can also be utilized as readily appreciated by one of ordinary skill in the art. These and other variations of the processing system  500  are readily contemplated by one of ordinary skill in the art given the teachings of the present principles provided herein. 
     Moreover, it is to be appreciated that systems  100 ,  200 ,  300 , and  400  described above with respect to  FIGS. 1, 2, 3, and 4 , respectively, are systems for implementing respective embodiments of the present principles. Part or all of processing system  500  may be implemented in one or more of the elements of any of systems  100 ,  200 ,  300 , and  400 . 
     Further, it is to be appreciated that system  500  may perform at least part of the method described herein including, for example, at least part of method  600  of  FIGS. 6-13 . Similarly, part or all of any of systems  200 ,  300 ,  400 , and/or  500  may be used to perform at least part of method  600  of  FIGS. 6-13 . 
       FIGS. 6-13  show an exemplary method  600  for tracking object detections, in accordance with an embodiment of the present principles. The method  600  can infer, from a single view (RGB image) of the object, the locations of keypoints in 2D and 3D spaces and their visibility. In an embodiment, step  605  can be considered to correspond to a training phase or stage, while steps  610 - 615  can be considered to correspond to a recognition phase or stage. Moreover, step  620  can be considered to be part of the recognition phase/stage. 
     At step  605 , form a Convolutional Neural Network (CNN) architecture which includes a multi-layer CNN which, in turn, jointly models multiple intermediate shape concepts. In an embodiment, the multiple intermediate shape concepts can include, but are not limited to, object pose, keypoint locations and visibility information. In an embodiment, spatial pooling can be removed/unused to avoid a loss of spatial information (see step  605 H), and each convolutional layer can be coupled with batch normalization and a Rectified Linear Unit (ReLU), which defines a term in the reformulated optimization problem of step  605 I. 
     In an embodiment, step  605  can include one or more of steps  605 A-I. 
     At step  605 A, receive a set of 3D object Computer Aided Design (CAD) models. 
     At step  605 B, render, based on the set of 3D CAD models, a set of synthetic images for training the CNN with corresponding intermediate shape concept labels. In an embodiment, step  605 B can be performed using a synthetic data generation pipeline which is configured to create synthetic data for the CNN (e.g., in the form of rendered synthetic images). In an embodiment, the rendered synthetic images can be rendered to be representative of realistic occlusion configurations caused by, e.g., multiple objects in close proximity, image boundary truncations, and/or so forth. In an embodiment, the rendered synthetic images can include, for example, but are not limited to, a rendered synthetic image of an object with its 2D keypoints and a rendered synthetic image of a 3D skeleton structure of the object. 
     In an embodiment, step  605 B can include one or more of steps  605 B 1 -B 3 . 
     Referring to  FIG. 7 , at step  605 B 1 , augment a synthetic data generation process (e.g., extend a synthetic data generation pipeline) with 2D/3D landmarks and visibility information. 
     At step  605 B 2 , overlay rendered synthetic images which are based on the synthetic data onto real image backgrounds (to avoid overfitting to the synthetic data). 
     At step  605 B 3 , crop the one or more objects from each rendered synthetic image and extract the object viewpoint, 2D/3D keypoint locations and their visibility states as training labels to provide a resultant set of rendered synthetic images which include pairs of input x and labels y for a supervised learning task to which the CNN can be applied. 
     At step  605 C, formulate an optimization problem for the multi-layer CNN that specifies the training data for use in training the CNN (for the supervised learning task). In an embodiment, the optimization problem can be formulated to further involve a problem specific loss  1 , a set of weights W of N layers of the CNN, and a function ƒ that is defined based on the network structure. 
     At step  605 D, augment the training set with (additional) supervisory signals. 
     Referring to  FIG. 8 , at step  606 E, deploy dropout between hidden convolutional layers of the CNN. In an embodiment, the dropout can be deployed to further address the problem of overfitting. 
     At step  605 F, in the CNN, include a Global Average Pooling (GAP) layer followed by one fully connected (FC) layer. 
     At step  605 G, select a sequence of conditions for 2D/3D keypoint prediction with growing complexity as intermediate shape concepts. In an embodiment, the sequence of conditions can be the following: (1) object viewpoint; (2) keypoint visibility; (3) 3D keypoint locations; and (4) a full set of 2D keypoint locations regardless of the visibility. These conditions/intermediate shape concepts are imposed on the CNN in order to deeply supervise the CNN at different/certain depths and minimize certain intermediate losses. These conditions/intermediate shape concepts can be considered to form a shape concept hierarchy, e.g., for governing the supervision of the CNN. Hence, step  605 G can involve explicitly enforcing a condition(s) that hidden layers of the CNN yield a sequence of known intermediate concepts with growing complexity towards the final task, using the augmented training set. This explicit enforcement provides regularization to, e.g., address the problem of overfitting. 
     At step  605 H, reformulate the optimization problem to be trained over the augmented training set. In an embodiment, the conditions/intermediate shape concepts can minimize four intermediate losses (each respectively relating to one of the above four specified conditions/intermediate shape concepts) in the reformulated optimization problem. 
     Referring to  FIG. 9 , at step  605 I, simultaneously back propagate the errors of all supervisory signals to weights of the CNN, in order to optimize the objective of the reformulated optimization problem. 
     At step  610 , receive a test (real) image depicting one or more objects. 
     At step  615 , perform intra-class appearance variation-aware and occlusion-aware 3D object parsing on the test image by applying the (trained) CNN to the test image to provide a set of output images. In an embodiment, the set of output images can include an image pair including a 2D geometric structure and a 3D geometric structure of each of the one or more objects depicted in the test image. In an embodiment, the 3D geometric structure can be a 3D skeleton, where semantically meaningful object parts (such as the wheels of a car) are represented by 3D keypoints and their connections define 3D structure of an object category. 
     At step  620 , perform one or more actions in response to the set of output images. For example, the one or more actions can be directed to one or more of the following: 3D object parsing; Advanced Driver-Assistance System (ADAS); surveillance; action recognition; and so forth. 
     In an embodiment, step  620  can include one or more of steps  620 A through  620 C. 
     Referring to  FIG. 10 , at step  620 A, corresponding to 3D object parsing, perform one or more actions that can include, but are not limited to, one or more of the following: provide a human-perceptible emphasis (e.g., highlighting, etc.) on an object portion of interest in the set of output images; sound an alarm due to the detection of a particular object portion (a firearm, the wrong portion in place of a correct portion, etc.) of an object (a user); perform one or more actions relating to an object or object portion in the set of output images; generate a listing of object portions that form the object (e.g., to form a parts list); order a missing part, where the part is determined to be missing from the set of output images; object reconstruction from less than all portions of a given object; and so forth. 
     Other actions relating to an Advanced Driver-Assistance System (ADAS), a surveillance system, and an action recognition system are also described herein below. 
     Referring to  FIG. 11 , at step  620 B, corresponding to an Advanced Driver-Assistance System (ADAS), perform one or more actions that can include, but are not limited to, one or more of the following: performing an action(s) to mitigate a potential harm relative to the vehicle (e.g., potential harm to the vehicle and/or its occupants and/or external objects (inanimate and animate objects). Such action(s) to mitigate potential harm can include, but is not limited to, making a suggestion to a user of the motor vehicle, e.g., regarding an action to take to avoid a collision or other undesired condition; automatically controlling one or more driving functions of the motor vehicle; etc. 
     For example, automatically control one or more driving functions responsive to the detection results. For example, in an embodiment, the present invention is integrated with and/or otherwise coupled to an Advanced Driver-Assistance System (ADAS). The ADAS could apply a decision making process to, e.g., objects and/or object portions depicted in the set of output images, in order to determine whether a dangerous condition(s) exists or not (with respect to the motor vehicle) and to further determine a proper corrective action to take to avoid or at least mitigate any potential harm that can result from the dangerous condition. The decision making process can be any known type of decision making process including, but not limited to, preprogrammed rules, a neural network, a decision tree, and so forth. It is to be appreciated that the preceding decision making processes are merely illustrative and, thus, other decision making processes can also be used in accordance with the teachings of the present invention, while maintaining the spirit of the present invention. 
     The control that can be imposed by step  620 B can involve, for example, but is not limited to, steering, braking, and accelerating functions. For example, responsive the location of an object or object portion directly in the path of the motor vehicle, the processor may initiate a control signal to the braking system to apply the brakes in order to avoid hitting the object or object portion with the motor vehicle. In another embodiment, when safe, the vehicle can be automatically steered by the processor initiating a control signal to the steering system. These and other implementations are readily determined by one of ordinary skill in the art, given the teachings of the present invention provided herein, while maintaining the spirit of the present invention. 
     Regarding the recommendations to the vehicle operator (e.g., brake now, brake hard, steer right, accelerate, etc.), these recommendations can be made visually and/or audibly. Hence, as can be envisioned by one of ordinary skill in the art, such recommendation can be directed to avoiding objects or object portions in a path of the motor vehicle (such as, e.g., an emergency vehicle or a non-emergency vehicle), where such objects or object portions can be inanimate or animate objects. 
     Referring to  FIG. 12 , at step  620 C, corresponding to surveillance, perform one or more actions that can include, but are not limited to, one or more of the following: log the detection of a possibly dangerous object or object portion or a prohibited object or object portion; generate a local or remote alarm indicative of the detection of a possibly dangerous object or object portion or a prohibited object or object portion; open a gate or door or window to permit access (to all or a portion of a target area) or close a gate or door or window to block access (to all or a portion of a target area) (and potentially detain an involved individual until the proper authorities can intervene); and so forth. 
     Referring to  FIG. 13 , at step  620 D, corresponding to action recognition, perform one or more (response) actions (with respect to the recognized action performed by an object or an object portion) that can include, but are not limited to, one or more of the following: providing an audible and/or visual alert; open a gate or door or window to permit access or close a gate or door or window to block access (and potentially detain or “free” an involved individual) to an area or a portion of the area; recommend improvements to the performance of the action that is recognized (e.g., recommending a different stance(s), hand position, foot position, etc. with respect to a sport, an object used in the sport, and so forth); identify an action that is incorrect and/or prohibited from being performed; recommend a correct and/or authorized action in place of a performed prohibited action; and so forth. 
     The system could apply a decision making process to, e.g., a list of object positions determined by step  620 A, in order to determine what type of action is being performed (in order to recognize the action) and what type of response action should be performed in response to the detected action type, based on the set of output images. The decision making process can be any known type of decision making process including, but not limited to, preprogrammed rules, a neural network, a decision tree, and so forth. It is to be appreciated that the preceding decision making processes are merely illustrative and, thus, other decision making processes can also be used in accordance with the teachings of the present invention, while maintaining the spirit of the present invention. Hence, the system could determine a person is performing a dangerous action (e.g., waiving a weapon (e.g., a knife, a firearm, etc.) and then secure the person within a target area or a region within a target area. The system could provide a user perceptible recommendation for a corrective action to the action performed by one or more users in order to optimize a performance of an action performed by the one or more users. The action can be a sports or any other type of action. 
     Regarding step  620  and its “sub-steps”, the preceding actions mentioned with respect thereto are merely illustrative and, thus, other actions can also be performed in response to object detection and tracking results. As is evident to one of ordinary skill in the art, the action(s) taken is(are) dependent upon the type of application to which the present invention is applied. 
     A description will now be given regarding further aspects of the present invention. 
     Monocular 3D object parsing is highly desirable in various scenarios including occlusion reasoning and holistic scene interpretation. The present invention provides a deep convolutional neural network (CNN) architecture to localize semantic parts in 2D image and 3D space while inferring their visibility states, given a single RGB image. A key insight is to exploit domain knowledge to regularize the network by deeply supervising its hidden layers, in order to sequentially infer intermediate concepts associated with the final task. To acquire training data in desired quantities with ground truth 3D shape and relevant concepts, 3D object CAD models are rendered to generate large-scale synthetic data and simulate challenging occlusion configurations between objects. 
     Herein, intermediate shape concepts pertinent to 2D/3D shape understanding, such as pose and part visibility, are applied to supervise intermediate layers of a CNN. This allows greater accuracy in localizing the semantic elements of an object observed in a single image. 
     To illustrate this idea, a 3D skeleton is used as the shape representation, where semantically meaningful object parts (such as the wheels of a car) are represented by 3D keypoints and their connections define 3D structure of an object category. This representation is more efficient than 3D volumes or meshes in conveying the semantic information necessary for shape reasoning in applications such as autonomous driving. 
     A novel CNN architecture is provided which jointly models multiple shape concepts including object pose, keypoint locations and visibility. A deep supervision framework is formulated by generalizing Deeply Supervised Nets. Herein, one particular network instance is described where convolutional layers at different depths are deeply supervised with intermediate shape concepts. Further, instead of using expensive manual annotations, it is proposed to render 3D CAD models to create synthetic images with intermediate shape concept labels and simulate the challenging occlusion configurations for robust occlusion reasoning. 
       FIG. 14  illustrates an overall framework  1400  of the present invention, in accordance with an embodiment of the present invention. The framework  1400  includes 3D CAD models  1410 , a rendering pipeline  1420 , a shape concept hierarchy  1430 , a set of synthetic images  1440 , and a Convolutional Neural Network (CNN)  1450 . An input (real) image  1461  is provided to the CNN  1450 , which then generates an image  1499 A of a 2D geometric structure of an object (or a portion thereof) and/or an image  1499 B of a 3D geometric structure of the object (or a portion thereof). Hence, in a test or recognition stage, the present invention provides accurate localization of semantic parts in 2D and/or 3D, while being robust to intra-class appearance variations as well as occlusions. 
       FIG. 15  illustrates an exemplary instance of deeply supervised CNN  1500  using shape concepts, in accordance with an embodiment of the present invention. 
     The CNN  1500  includes a learning network  1510 , also referred to herein as “DISCO”, which stands for Deep supervision with Intermediate Shape COncepts. 
     The CNN  1500  further includes a rendering pipeline  1520 . 
     The learning network  1510  includes convolutional blocks [(CONV−A)×B]  1511 , dropout portions  1512 , GAPs  1513 , and Fully Connected (FC) layers  1514 . In blocks  1511 , the “A” and the “B” in “(CONV−A)×B” means A stacked convolutional layers with filters of size B×B. 
     The rendering pipeline  1520  includes and/or otherwise involves 3D keypoints  1521 , CAD models  1522 , a render engine  1523 , synthesized data  1524 , a real image  1525 , 2D keypoint locations  1531 , 3D keypoint locations  1532 , keypoint visibility  1533 , viewpoint  1534 , and a 2D rendered image  1535 . 
     An output  1599  from the CNN  1500  includes an image of 2D keypoints  1599 A and an image of a 3D skeleton structure  1599 B. 
     At test time, DISCO trained on only synthetic images generalizes well to real images. In particular, DISCO empirically outperforms single-task architectures without supervision for intermediate shape concepts and multitask networks which impose supervision of all the concepts at the last layer. This observation demonstrates the intimacy of shape concepts for 3D object parsing, despite the fact that we ignore aspects of photorealism such as material and illumination in our rendered training data. 
     It is noted that most existing approaches estimate 3D geometry by comparing projections of parameterized shape models with separately predicted 2D patterns, such as keypoint locations or heat maps. This makes prior methods sensitive to partial view ambiguity and incorrect 2D structure predictions. Moreover, scarce 3D annotations for real images further limit their performance. In contrast, the present invention makes the following novel contributions to alleviate those problems: 
     (1) We demonstrate the utility of rendered data with access to intermediate shape concepts. In addition, we model occlusions by appropriately rendering multiple object configurations, which presents a novel way of exploiting 3D CAD data for realistic scene interpretation. 
     (2) We apply intermediate shape concepts to deeply supervise the hidden layers of a CNN. This approach exhibits the better generalization from synthetic to real images than the standard end-to-end training. 
     (3) Our method achieves state-of-the-art performance on 2D/3D semantic part localization under occlusion and large appearance changes on several public benchmarks. 
     A description will now be given regarding deep supervision with shape concepts, in accordance with an embodiment of the present invention. 
     Herein, a novel CNN architecture is provided for 3D shape parsing which incorporates constraints through intermediate shape concepts such as object pose, keypoint locations, and visibility information. A goal of the present invention is to infer, from a single view (RGB image) of the object, the locations of keypoints in 2D and 3D spaces and their visibility. 
     A description will now be given regarding deep supervision, in accordance with an embodiment of the present invention. 
     In an embodiment, the present invention sequentially applies deep supervision on intermediate concepts intrinsic to the ultimate task, in order to regularize the network for better generalization. 
     Let  ={(x, y)} represent the training set with pairs of input x and labels y for a supervised learning task. The associated optimization problem for a multi-layer CNN is as follows: 
                     W   *     =       min   W     ⁢       ∑       (     x   ,   y     )     ∈   𝒵               ⁢     l   ⁡     (     y   ,     f   ⁡     (     x   ,   W     )         )                   (   1   )               
where l(., .) is a problem specific loss, W={W 1 , . . . , W N } stands for the weights of N layers, and function ƒ is defined based on the network structure. In practice, the optimal solution Ŵ* may suffer from overfitting. That is, given a new population of data  ′, the performance of ƒ(⋅, W) on  ′ is substantially lower than on  . This is particularly the case when, for example, we train on synthetic data but test on real data.
 
     One way to address the overtraining is through regularization which biases the network to incrementally reproduce physical quantities that are relevant to the final answer. For example, object pose is an indispensable element to predict 3D keypoint locations. Intuitively, the idea is to prefer solutions that reflect the underlying physical structure of the problem which is entangled in the original training set. Since deeper layers in CNNs represent more complex concepts due to growing size of receptive fields and more non-linear transformations stacked along the way, we may realize our intuition by explicitly enforcing that hidden layers yield a sequence of known intermediate concepts with growing complexity towards the final task. 
     To this end, we define the augmented training set  ={(x, {y 1 , . . . , y N })} with additional supervisory signals {y 1 , . . . , y N-1 }. Further, we denote W 1:i ={W 1 , . . . , W i } as the weights for the first i layers of the CNN and hi=ƒ(⋅, W 1:i ) as the activation map of layer i. We now extend (1) to the additional training signals y i  by introducing functions y i =g(h i , v i ) parameterized by the weight v i . Letting V={v 1 , . . . , v N-1 }, we can now write a new objective trained over  : 
     
       
         
           
             
               
                 
                   
                     
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     The above objective can be optimized by simultaneously back propagating the errors of all supervisory signals scaled by λ i  on each l t  to W 1:i . From the perspective of the original problem, new constraints through y i  act as additional regularization on the hidden layers, thus biasing the network toward solutions that exhibit better generalization than solutions to Equation (1). 
     A description will now be given regarding network architecture, in accordance with an embodiment of the present invention. 
     To set up Equation (2), we must first choose a sequence of necessary conditions for 2D/3D keypoint prediction with growing complexity as intermediate shape concepts. We have chosen, in order, (1) object viewpoint, (2) keypoint visibility, (3) 3D keypoint locations and (4) full set of 2D keypoint locations regardless of the visibility. We impose this sequence of intermediate concepts to deeply supervise the network at certain depths and minimize four intermediate losses l i  in Equation (2), with other losses removed. 
     Our network resembles a VGG neural network and includes deeply stacked 3×3 convolutional layers. However, unlike VGG, we remove local spatial pooling and couple each convolutional layer with batch normalization and ReLU, which defines the ƒ(x, W 1:i ) in Equation (2). This is motivated by the intuition that spatial pooling leads to the loss of spatial information. Further, g(h i , v i ) is constructed with one global average pooling (GAP) layer followed by one fully connected (FC) layer with 512 neurons, which is different from stacked FC layers in VGG. These two changes are critical to significantly improve the performance of VGG like networks for 2D/3D landmark localization. 
     To further reduce the issue of overfitting, we deploy dropout between the hidden convolutional layers. At layers 4, 8, 12, we perform the down sampling using convolution layers with stride 2.  FIG. 15  (bottom-left) illustrates our network architecture in detail. We use L2 loss at all points of supervision. “(Conv−A)×B” means A stacked convolutional layers with filters of size B×B. We deploy 25 convolutional layers in total. 
     In experiments, we only consider the azimuth angle of the object viewpoint with respect to a canonical pose. We further discretize the azimuth angle into M bins and regress it to a one-hot encoding (the entry corresponding to the predicted discretized pose is set to 1 and all others to 0). Keypoint visibility is also represented by a binary vector with 1 indicating occluded state of a keypoint. 2D keypoint locations are normalized to [0, 1] with the image size along the width and height dimensions. We center 3D keypoint coordinates of a CAD model at the origin and scale them to set the longest dimension (along X,Y,Z) to unit length. CAD models are assumed to be aligned along the principal coordinate axes, and registered to a canonical pose, as is the case for the ShapeNet dataset. During training, each loss is back propagated to train the network jointly. 
     A description will now be given regarding synthetic data generation, in accordance with an embodiment of the present invention. 
     Our approach can use a large amount of training data because it is based on deep CNNs and involves more fine-grained labels than other visual tasks such as object classification. Furthermore, we aim for the method to work with occluded test cases. Therefore, we need to generate training examples that are representative of realistic occlusion configurations caused by multiple objects in close proximity as well as image boundary truncations. To obtain such large-scale training data, we extend the data generation pipeline of “Render for CNN” with 2D/3D landmarks and visibility information. 
     In the rendering process, we pick a small subset of CAD models from ShapeNet for a given object category and manually annotate 3D keypoints on each CAD model. Next, we render each CAD model using the open-source tool Blender while randomly sampling rendering parameters from a uniform distribution including camera viewpoint, number/strength of light sources, and surface gloss reflection. Finally, we overlay the rendered images on real image backgrounds to avoid overfitting to synthetic data. We crop the object from each rendered image and extract the object viewpoint, 2D/3D keypoint locations and their visibility states from the render engine as the training labels. 
     To model multi-object occlusion, we randomly select two different object instances and place them close to each other without overlapping in 3D space. During rendering, we compute the occlusion ratio of each instance by calculating the fraction of visible 2D area versus the complete 2D projection of CAD model. Keypoint visibility is computed by ray-tracing. We select instances with occlusion ratios ranging from 0.4 to 0.9. For truncation, we randomly select two image boundaries (left, right, top, or bottom) of the object and shift them by [0, 0.3] of the image size along that dimension. 
     Embodiments described herein may be entirely hardware, entirely software or including both hardware and software elements. In a preferred embodiment, the present invention is implemented in software, which includes but is not limited to firmware, resident software, microcode, etc. 
     Embodiments may include a computer program product accessible from a computer-usable or computer-readable medium providing program code for use by or in connection with a computer or any instruction execution system. A computer-usable or computer readable medium may include any apparatus that stores, communicates, propagates, or transports the program for use by or in connection with the instruction execution system, apparatus, or device. The medium can be magnetic, optical, electronic, electromagnetic, infrared, or semiconductor system (or apparatus or device) or a propagation medium. The medium may include a computer-readable medium such as a semiconductor or solid state memory, magnetic tape, a removable computer diskette, a random access memory (RAM), a read-only memory (ROM), a rigid magnetic disk and an optical disk, etc. 
     It is to be appreciated that the use of any of the following “/”, “and/or”, and “at least one of”, for example, in the cases of “A/B”, “A and/or B” and “at least one of A and B”, is intended to encompass the selection of the first listed option (A) only, or the selection of the second listed option (B) only, or the selection of both options (A and B). As a further example, in the cases of “A, B, and/or C” and “at least one of A, B, and C”, such phrasing is intended to encompass the selection of the first listed option (A) only, or the selection of the second listed option (B) only, or the selection of the third listed option (C) only, or the selection of the first and the second listed options (A and B) only, or the selection of the first and third listed options (A and C) only, or the selection of the second and third listed options (B and C) only, or the selection of all three options (A and B and C). This may be extended, as readily apparent by one of ordinary skill in this and related arts, for as many items listed. 
     Having described preferred embodiments of a system and method (which are intended to be illustrative and not limiting), it is noted that modifications and variations can be made by persons skilled in the art in light of the above teachings. It is therefore to be understood that changes may be made in the particular embodiments disclosed which are within the scope and spirit of the invention as outlined by the appended claims. Having thus described aspects of the invention, with the details and particularity required by the patent laws, what is claimed and desired protected by Letters Patent is set forth in the appended claims.