English:
[0001] Sensing occupancy in a conference room or office space is helpful for monitoring and improving worker experiences. Attendees of formally arranged and ad hoc gatherings want to have a seamless experience at all phases of the meeting (start, middle, ending). Sensors may be deployed in an attempt at automating these phases. Badge readers, Bluetooth Low Energy (BLE) beacons, ultrasonic communications, etc., may be used. These methods involve the meeting attendee either taking an action, or carrying an object or device to conduct a meeting.
[0002] Figure 1 is a block diagram illustrating a computing system with a 360- degree camera and a neural network for object identification according to one example.
[0003] Figure 2 is a diagram illustrating a simplified representation of a 360- degree camera image in dual fisheye format according to one example.
[0004] Figure 3 is a diagram illustrating a simplified representation of a rectangular image in equirectangular format according to one example.
[0005] Figure 4 is a diagram illustrating a simplified representation of a modified rectangular image in equirectangular format with side edge duplication according to one example.
[0006] Figure 5 is a flow diagram illustrating a method for identifying objects appearing in an image according to one example.
[0007] In the following detailed description, reference is made to the accompanying drawings which form a part hereof, and in which is shown by way of illustration specific examples in which the disclosure may be practiced. It is to be understood that other examples may be utilized and structural or logical changes may be made without departing from the scope of the present disclosure. The following detailed description, therefore, is not to be taken in a limiting sense, and the scope of the present disclosure is defined by the appended claims. It is to be understood that features of the various examples described herein may be combined, in part or whole, with each other, unless specifically noted otherwise.
[0010] Neural networks may be used on images to identify the presence and identity of people, as well as to analyze a speaker and the level of attention of participants. Other parameters may also be analyzed. Neural networks developed for object recognition in images and video generally work well on standard formats (e.g., 4:3 and 16:9 ratios), but may not work as well for 360- degree camera images. Some examples disclosed herein are directed to improving object recognition by neural networks that are applied to 360-degree camera images (still images or video images) for inferencing. The 360-degree views captured by the camera are represented by a rectangular image (e.g., equirectangular image or toroidal image). An object appearing near the edge of the rectangular image may be split, such that a portion of the object appears at the left edge and another portion appears at the right edge. This can prevent the neural network from properly recognizing the object. In one example, an edge portion from a first edge (e.g., left edge) of the rectangular image is copied to a second edge (e.g., right edge) of the rectangular image to form a modified rectangular image. For objects that are split between the left and right edges, these objects appear as complete objects in the modified rectangular image and are properly recognized by the neural network. Standard object recognition neural networks may be used on such modified images without re-training.
[0011] Figure 1 is a block diagram illustrating a computing system 100 with a 360-degree camera 136 and a neural network 108 for object identification according to one example. Computing system 100 includes at least one processor 102, a memory 104, input devices 130, output devices 132, display 134, and 360-degree camera 136. In the illustrated example, processor 102, memory 104, input devices 130, output devices 132, display 134, and 360- degree camera 136 are communicatively coupled to each other through communication link 128.
[0012] Input devices 130 include a keyboard, mouse, data ports, and/or other suitable devices for inputting information into system 100. Output devices 132 include speakers, data ports, and/or other suitable devices for outputting information from system 100. Display 134 may be any type of display device that displays information to a user of computing system 100.
[0013] 360-degree camera 136 generates 360-degree camera images 1 16, which may be stored in memory 104. In one example, 360-degree camera 136 is a dual fish-eye spherical camera with two image sensors (e.g., positioned back-to-back and each having a field of view sufficient to provide complete 360- degree coverage), and produces 360-degree camera images in a dual fisheye format. In another example, 360-degree camera 136 includes a single image sensor with a mirror, and produces 360-degree camera images in a toroidal format. When used in a conferencing scenario, for example, the 360-degree camera 136 may be positioned near a center of the room to capture images of the complete room.
[0014] Processor 102 includes a central processing unit (CPU) or another suitable processor. In one example, memory 104 stores machine readable instructions executed by processor 102 for operating the system 100. Memory 104 includes any suitable combination of volatile and/or non-volatile memory, such as combinations of Random Access Memory (RAM), Read-Only Memory (ROM), flash memory, and/or other suitable memory. These are examples of non-transitory computer readable storage media. The memory 104 is non- transitory in the sense that it does not encompass a transitory signal but instead is made up of at least one memory component to store machine executable instructions for performing techniques described herein.
[0015] Memory 104 stores conferencing application module 106, neural network module 108, image modification module 1 10, object identification information 1 12, training images 1 14, 360-degree camera images 1 16, rectangular images 1 18, and modified rectangular images 120. Processor 102 executes instructions of modules 106, 108, and 1 10 to perform techniques described herein. It is noted that some or all of the functionality of modules 106, 108, and 1 10 may be implemented using cloud computing resources. Any of the images 1 14, 1 16, 1 18, and 120 may also be used in a cloud compute environment. For example, the training images 1 14 may be used in a cloud compute environment, or other high performance training computer environment, to train the neural network 108.
[0016] Conferencing application module 106 allows users to conduct audio and video conferences with remote participants. Neural network module 108 includes at least one neural network that is trained on a set of training images 1 14, and after being trained, performs object identification functions or other functions using other images, such as images 1 16, 1 18, or 120. Neural network module 108 may be used to track occupancy, people count, and person identification, and output object identification information 1 12 indicative of the tracked information. 360-degree camera images 1 16 are generated by 360- degree camera 136. Rectangular images 1 18 may be produced directly by 360- degree camera 136 (e.g., in a toroidal format), or may be produced by image modification module 110 by modifying non-rectangular images produced by 360-degree camera 136 (e.g., images in a dual fisheye format). Modified rectangular images 120 are produced by image modification module 1 10 by modifying rectangular images, such as by copying an edge portion from a first edge of a rectangular image, and pasting the copied edge portion to a second edge of the rectangular image to form a modified rectangular image.
[0017] In some examples, as a person or multiple people enter a conference room or other room containing computing system 100, the computing system 100 will detect that the room is occupied based on images captured by camera 136. This may be used to signal conferencing application 106 or a management system to track usage of the room. The conferencing application 106 may use this action as a wake signal to welcome the occupants, which provides a smooth experience whether a meeting has been scheduled or the people are entering for an ad-hoc meeting.
[0018] In addition to detecting occupancy, the computing system 100 can provide a count of people in the room based on images captured by camera 136. This is useful for tracking under or over-utilization of rooms. This information can also recommend to schedulers that a regular meeting should move to a larger or smaller room in the future.
[0019] Computing system 100 may also perform identification of a person by name as a form of authentication. Facial recognition may be performed by neural network module 108 based on images captured by camera 136 to determine the identity of people appearing in the images. A person entering the room may not be allowed to start a meeting unless they are determined by the system 100 to be on the participant list. Once the meeting has started, the system 100 can generate a flag if a person not on the list is in the room.
[0020] In one example, the various subcomponents or elements of the system 100 may be embodied in a plurality of different systems, where different modules may be grouped or distributed across the plurality of different systems. To achieve its desired functionality, system 100 may include various hardware components. Among these hardware components may be a number of processing devices, a number of data storage devices, a number of peripheral device adapters, and a number of network adapters. These hardware components may be interconnected through the use of a number of busses and/or network connections. The processing devices may include a hardware architecture to retrieve executable code from the data storage devices and execute the executable code. The executable code may, when executed by the processing devices, cause the processing devices to implement at least some of the functionality disclosed herein. Although some examples disclosed herein relate to audio and video conferencing, other examples may be applied to other scenarios.
[0021] Figure 2 is a diagram illustrating a simplified representation of a 360- degree camera image 1 16(1 ) in dual fisheye format according to one example. The dual fisheye format is the raw feed of a dual fisheye camera, which is one example of 360-degree camera 136 (Figure 1 ), without mapping into a flat equirectangular image.
[0022] Figure 3 is a diagram illustrating a simplified representation of a rectangular image 1 18(1 ) in equirectangular format according to one example. The equirectangular format is a mapping, performed by image modification module 1 10, of a 360-degree camera image in dual fisheye format, such as image 1 16(1 ) shown in Figure 2, into a flat image. This mapping is accurate around the“equator”, but the distortion at the“poles” may be very distinct. The rectangular image 1 18(1 ) includes a left edge 302 and a right edge 304, and a left edge image portion 306. As shown in Figure 3, a man appearing primarily near the right edge 304 of the image 1 18(1 ) is split, such that the majority of the man’s upper body appears near the right edge 304 and a smaller portion of the man’s upper body (i.e., a portion of his left arm) appears near the left edge 304. This can prevent the neural network module 108 from properly recognizing the man. This issue may be addressed as further described below with reference to Figure 4.
[0023] Figure 4 is a diagram illustrating a simplified representation of a modified rectangular image 120(1 ) in equirectangular format with side edge duplication according to one example. Modified rectangular image 120(1 ) is created by image modification module 1 10 (Figure 1 ) by modifying rectangular image 1 18(1 ) (Figure 3). Specifically, image modification module 1 10 copies the left edge image portion 306 from the left edge 302 of the rectangular image 1 18(1 ), and pastes this image portion 306 to the right edge 304 of the rectangular image 1 18(1 ) to form the modified rectangular image 120(1 ) with a new right edge image portion 408 and a new right edge 404. In the modified rectangular image 120(1 ), the man appearing in the image has his complete upper body shown near the right edge 404, rather than being split between the right and left edges as shown in Figure 3. This image modification facilitates recognition of the man by the neural network 108.
[0025] In some examples, training images 114 are not 360-degree images, but rather are in a standard format, such as images captured using a 50mm field of view lens, and having a 4:3 ratio. In these examples, the neural network module 108 is trained on the non-360-degree images, but is later used to perform inferencing on 360-degree images 1 16. In some examples, the edge duplication process described above with reference to Figure 3 and 4 is applied both to the training images 1 14, as well as the images used by the neural network module 108 for inferencing. In other examples, the edge duplication process is not used, and the training images 1 14 and the images used by the neural network module 108 for inferencing are both the same type of 360- degree images (e.g., dual fisheye, equirectangular, or toroidal). In other examples, the training images 1 14 are non-360-degree images that are computationally distorted and converted into synthetic 360-degree images that simulate actual 360-degree images from the raw feed of a 360-degree camera, and these synthetic 360-degree images are used to train the neural network 108 for inferencing on actual 360-degree images.
[0026] One example is directed to a method for identifying objects appearing in an image. Figure 5 is a flow diagram illustrating a method 500 for identifying objects appearing in an image according to one example. In one example, computing system 100 performs method 500. At 502 in method 500, a 360- degree image that was captured by a 360-degree camera is received. At 504, the 360-degree image is converted into a rectangular image. At 506, an edge portion from a first edge of the rectangular image is copied and pasted to a second edge of the rectangular image, thereby forming a modified rectangular image. At 508, a neural network is applied to the modified rectangular image to identify objects appearing in the modified rectangular image, wherein the modified rectangular image facilitates object identification near the second edge.
[0027] The 360-degree image in method 500 may be in a dual fisheye format. The 360-degree image in method 500 may be in a toroidal format. The rectangular image in method 500 may be in an equirectangular format.
[0028] The method 500 may further include identifying whether a room is occupied based on an output of the neural network. The method 500 may further include identifying a total number of people in a room based on an output of the neural network. The objects in method 500 may be people, and the method 500 may further include performing a facial recognition function with the neural network to determine identities of the people.
[0029] The method 500 may further include providing a plurality of training images; and for each training image, copying an edge portion from a first edge of the training image and pasting the copied edge portion to a second edge of the training image, thereby forming a plurality of modified training images. The method 500 may further include training the neural network on the plurality of modified training images prior to applying the neural network to the modified rectangular image. The method 500 may further include training the neural network on training images that are non-360-degree images. The method 500 may further include training the neural network on training images that are non- 360-degree images that are computationally distorted and converted into synthetic 360-degree images that simulate actual 360-degree images.
[0030] Another example is directed to a system, which includes a 360-degree camera to generate a 360-degree image. The system includes an image modification module to convert the 360-degree image into a rectangular image, copy an edge portion from a first edge of the rectangular image, and paste the copied edge portion to a second edge of the rectangular image, thereby forming a modified rectangular image that facilitates object identification near the second edge. The system includes a neural network to receive the modified rectangular image as input and identify objects appearing in the modified rectangular image. The 360-degree image may be in one of a dual fisheye format or a toroidal format.
[0031] Yet another example is directed to a non-transitory computer-readable storage medium storing instructions that, when executed by a processor, cause the processor to: receive a 360-degree image in a rectangular format; copy an image portion near a first edge of the image; paste the copied image portion near a second edge of the image, thereby forming a modified image; and analyze the modified image with at least one neural network to identify a total number of people appearing in the modified image. The non-transitory computer-readable storage medium may further store instructions that, when executed by a processor, further cause the processor to: analyze the modified image with the at least one neural network to determine identities of the people appearing in the modified image.
[0032] Although specific examples have been illustrated and described herein, a variety of alternate and/or equivalent implementations may be substituted for the specific examples shown and described without departing from the scope of the present disclosure. This application is intended to cover any adaptations or variations of the specific examples discussed herein. Therefore, it is intended that this disclosure be limited only by the claims and the equivalents thereof.

中文:
[0001]感测会议室或办公室空间中的占用情况有助于监测和改善工作人员的体验。正式安排的和临时聚集的出席者想要在会议的所有阶段(开始、中间、结束)处都具有无缝的体验。可以部署传感器来尝试使这些阶段自动化。可以使用徽章(badge)读取器、蓝牙低功耗(BLE)信标、超声通信等。这些方法涉及会议出席者采取动作、或者携带对象或设备进行会议。
[0002]图1是图示了根据一个示例的具有360度相机和神经网络以用于对象标识的计算系统的框图。
[0003]图2是图示了根据一个示例的采用双鱼眼格式的360度相机图像的简化表示的示图。
[0004]图3是图示了根据一个示例的采用等矩形格式的矩形图像的简化表示的示图。
[0005]图4是图示了根据一个示例的在利用侧边缘复制的情况下的采用等矩形格式的修改后的矩形图像的简化表示的示图。
[0006]图5是图示了根据一个示例的用于标识出现在图像中的对象的方法的流程图。
[0007]在以下具体实施方式中,参考了形成其一部分的附图,并且其中通过图示的方式示出了其中可以实践本公开的具体示例。要理解的是,在不偏离本公开的范围的情况下,可以利用其他示例,并且可以做出结构上的或逻辑上的改变。因此,不应在限制性的意义上理解以下具体实施方式,并且本公开的范围由所附权利要求来限定。要理解的是,本文中描述的各种示例的特征可以与彼此部分或全部地组合,除非另行具体地指出。
[0010]神经网络可以被用在图像上以标识人员的存在和身份,以及分析说话者和参与者的注意力水平。还可以分析其他参数。针对图像和视频中的对象识别而开发的神经网络通常在标准格式(例如,4:3和16:9的比例)上工作良好,但是对于360度相机图像可能无法良好地工作。本文中公开的一些示例涉及通过神经网络来改善对象识别,该神经网络被应用于360度相机图像(静止图像或视频图像)以进行推理。相机所捕获的360度视图由矩形图像(例如,等矩形图像或环形图像)来表示。出现在矩形图像的边缘附近的对象可能被分割,使得该对象的一部分出现在左边缘处,而另一部分出现在右边缘处。这可能会阻止神经网络正确地识别该对象。在一个示例中,来自矩形图像的第一边缘(例如,左边缘)的边缘部分被拷贝到矩形图像的第二边缘(例如,右边缘)以形成修改后的矩形图像。对于被分割在左边缘与右边缘之间的对象,这些对象在修改后的矩形图像中作为完整的对象出现,并且被神经网络正确地识别。标准的对象识别神经网络可以在这种修改后的图像上使用,而无需重新训练。
[0011]图1是图示了根据一个示例的具有360度相机136和神经网络108以用于对象标识的计算系统100的框图。计算系统100包括:至少一个处理器102、存储器104、输入设备130、输出设备132、显示器134、以及360度相机136。在所图示的示例中,处理器102、存储器104、输入设备130、输出设备132、显示器134、以及360度相机136通过通信链路128彼此通信地耦合。
[0012]输入设备130包括:键盘、鼠标、数据端口、和/或用于将信息输入到系统100中的其他合适设备。输出设备132包括:扬声器、数据端口、和/或用于从系统100输出信息的其他合适设备。显示器134可以是向计算系统100的用户显示信息的任何类型的显示设备。
[0013]360度相机136生成360度相机图像116,可以将图像116存储在存储器104中。在一个示例中,360度相机136是具有两个图像传感器(例如,背对背定位,并且每个传感器具有足以提供完整360度覆盖的视场)的双鱼眼球形相机,并且产生采用双鱼眼格式的360度相机图像。在另一个示例中,360度相机136包括具有镜子的单个图像传感器,并且产生采用环形格式的360度相机图像。360度相机136例如当在会议场景中被使用时可以位于房间的中心附近以捕获完整房间的图像。
[0014]处理器102包括中央处理单元(CPU)或另一个合适的处理器。在一个示例中,存储器104存储由处理器102执行以用于操作系统100的机器可读指令。存储器104包括易失性和/或非易失性存储器的任何合适的组合,诸如随机存取存储器(RAM)、只读存储器(ROM)、闪速存储器、和/或其他合适存储器的组合。这些是非暂时性计算机可读存储介质的示例。存储器104在其不涵盖暂时性信号的意义上是非暂时性的,但是代替地由至少一个存储器组件组成,以存储用于实行本文中描述的技术的机器可执行指令。
[0015]存储器104存储会议应用模块106、神经网络模块108、图像修改模块110、对象标识信息112、训练图像114、360度相机图像116、矩形图像118、以及修改后的矩形图像120。处理器102执行模块106、108和110的指令以实行本文中描述的技术。要注意的是,可以使用云计算资源来实现模块106、108和110的一些或全部功能。图像114、116、118和120中的任一个也可以在云计算环境中使用。例如,训练图像114可以在云计算环境或其他高性能训练计算机环境中使用,以训练神经网络108。
[0016]会议应用模块106允许用户与远程参与者进行音频和视频会议。神经网络模块108包括至少一个神经网络,该至少一个神经网络是在一组训练图像114上训练的,并且在被训练之后,该至少一个神经网络使用其他图像(诸如,图像116、118或120)来实行对象标识功能或其他功能。神经网络模块108可以被用来跟踪占用情况、人员计数和人员标识,并且输出指示所跟踪的信息的对象标识信息112。360度相机图像116由360度相机136来生成。矩形图像118可以由360度相机136直接产生(例如,采用环形格式),或者可以由图像修改模块110通过修改360度相机136所生成的非矩形图像(例如,采用双鱼眼格式的图像)来产生。修改后的矩形图像120由图像修改模块110通过修改矩形图像(诸如,通过拷贝来自矩形图像的第一边缘的边缘部分,并且将所拷贝的边缘部分粘贴到矩形图像的第二边缘,以形成修改后的矩形图像)来产生。
[0017]在一些示例中,当一个人员或多个人员进入会议室或包含计算系统100的其他房间时,计算系统100将基于由相机136捕获的图像来检测该房间是否被占用。这可以被用来发信号通知会议应用106或管理系统以跟踪该房间的使用情况。会议应用106可以使用该动作作为欢迎占用者的唤醒信号,无论是已经安排了会议还是人员正在进入以参加临时会议,这都提供了流畅的体验。
[0018]除了检测占用情况之外,计算系统100还可以基于由相机136捕获的图像来提供该房间中的人员计数。这对于跟踪房间使用不足或过度使用而言是有用的。该信息还可以向日程安排者(scheduler)建议例行会议在未来应当移动到更大或更小的房间。
[0019]计算系统100还可以按姓名来实行对人员的标识作为一种形式的认证。可以由神经网络模块108基于相机136所捕获的图像来实行面部识别,以确定出现在图像中的人员的身份。可能不允许进入该房间的人员开始会议,除非他们被系统100确定为在参与者列表上。一旦会议已经开始,如果房间中有不在该列表上的人员,则系统100可以生成标记。可以通知经授权的用户以用于采取动作(如果他们愿意的话),并且系统100可以记录这些事件以用于进一步的分析或动作。
[0020]在一个示例中,系统100的各种子组件或元件可以体现在多个不同的系统中,其中不同的模块可以跨该多个不同的系统而分组或分布。为了实现其期望的功能,系统100可以包括各种硬件组件。在这些硬件组件当中的可以是多个处理设备、多个数据存储设备、多个外围设备适配器和多个网络适配器。这些硬件组件可以通过使用多个总线和/或网络连接而互连。处理设备可以包括用以从数据存储设备检索可执行代码并且执行该可执行代码的硬件架构。该可执行代码在由处理设备执行时可以使处理设备实现本文中公开的功能中的至少一些。尽管本文中公开的一些示例与音频和视频会议有关,但是其他示例可以应用于其他场景。
[0021]图2是图示了根据一个示例的采用双鱼眼格式的360度相机图像116(1)的简化表示的示图。双鱼眼格式是双鱼眼相机(其是360度相机136(图1)的一个示例)的原始馈送,而没有映射到平面等矩形图像中。
[0022]图3是图示了根据一个示例的采用等矩形格式的矩形图像118(1)的简化表示的示图。等矩形格式是采用双鱼眼格式的360度相机图像(诸如,图2中所示的图像116(1))到平面图像中的映射,该映射由图像修改模块110实行。这种映射在“赤道”附近是准确的,但是在“极点”处的扭曲(distortion)可能非常明显。矩形图像118(1)包括左边缘302和右边缘304、以及左边缘图像部分306。如图3中所示,主要出现在图像118(1)的右边缘304附近的男性被分割,使得该男性的上半身的大部分出现在右边缘304附近,而该男性的上半身的较小部分(即,其左臂的一部分)出现在左边缘304附近。这可能会防止神经网络模块108正确地识别该男性。该问题可以被解决,如下面参考图4进一步描述的那样。
[0023]图4是图示了根据一个示例的在利用侧边缘复制的情况下的采用等矩形格式的修改后的矩形图像120(1)的简化表示的示图。修改后的矩形图像120(1)由图像修改模块110(图1)通过修改矩形图像118(1)(图3)来创建。具体地,图像修改模块110拷贝来自矩形图像118(1)的左边缘302的左边缘图像部分306,并且将该图像部分306粘贴到矩形图像118(1)的右边缘304,以形成具有新的右边缘图像部分408和新的右边缘404的修改后的矩形图像120(1)。在修改后的矩形图像120(1)中,出现在该图像中的男性具有在右边缘404附近示出的其完整上半身,而不是如图3中所示的那样被分割在左边缘与右边缘之间。该图像修改便于神经网络108对该男性的识别。
[0025]在一些示例中,训练图像114不是360度图像,而是采用标准格式,诸如使用50 mm视场的镜头捕获的并且具有4:3比例的图像。在这些示例中,神经网络模块108是在非360度图像上训练的,但是随后被用于在360度图像116上实行推理。在一些示例中,上面参考图3和图4描述的边缘复制过程被应用于训练图像114、以及由神经网络模块108用于推理的图像两者。在其他示例中,不使用边缘复制过程,并且训练图像114、以及由神经网络模块108用于推理的图像两者都是相同类型的360度图像(例如,双鱼眼、等矩形或环形的)。在其他示例中,训练图像114是非360度图像,这些非360度图像在计算上被扭曲并且被转换成合成360度图像,这些合成360度图像模拟了来自360度相机的原始馈送的实际360度图像,并且这些合成360度图像被用来训练神经网络108以用于在实际360度图像上进行推理。
[0026]一个示例涉及一种用于标识出现在图像中的对象的方法。图5是图示了根据一个示例的用于标识出现在图像中的对象的方法500的流程图。在一个示例中,计算系统100实行方法500。在方法500中的502处,接收由360度相机捕获的360度图像。在504处,将360度图像转换成矩形图像。在506处,拷贝来自矩形图像的第一边缘的边缘部分并且将该边缘部分粘贴到矩形图像的第二边缘,从而形成修改后的矩形图像。在508处,将神经网络应用于修改后的矩形图像,以标识出现在修改后的矩形图像中的对象,其中修改后的矩形图像便于在第二边缘附近进行对象标识。
[0027]方法500中的360度图像可以采用双鱼眼格式。方法500中的360度图像可以采用环形格式。方法500中的矩形图像可以采用等矩形格式。
[0028]方法500可以进一步包括:基于神经网络的输出来标识房间是否被占用。方法500可以进一步包括:基于神经网络的输出来标识房间中的人员的总数。方法500中的对象可以是人员,并且方法500可以进一步包括:利用神经网络来实行面部识别功能以确定人员的身份。
[0029]方法500可以进一步包括:提供多个训练图像;以及针对每个训练图像,拷贝来自训练图像的第一边缘的边缘部分,并且将所拷贝的边缘部分粘贴到训练图像的第二边缘,从而形成多个修改后的训练图像。方法500可以进一步包括:在将神经网络应用于修改后的矩形图像之前,在该多个修改后的训练图像上训练神经网络。方法500可以进一步包括:在作为非360度图像的训练图像上训练神经网络。方法500可以进一步包括:在作为非360度图像的训练图像上训练神经网络,所述非360度图像在计算上被扭曲并且被转换成模拟了实际360度图像的合成360度图像。
[0030]另一个示例涉及一种系统,该系统包括用以生成360度图像的360度相机。该系统包括:图像修改模块,用以将360度图像转换成矩形图像,拷贝来自矩形图像的第一边缘的边缘部分,并且将所拷贝的边缘部分粘贴到矩形图像的第二边缘,从而形成便于在第二边缘附近进行对象标识的修改后的矩形图像。该系统包括:神经网络,用以接收修改后的矩形图像作为输入,并且标识出现在修改后的矩形图像中的对象。360度图像可以采用双鱼眼格式或环形格式之一。
[0031]又一个示例涉及一种存储指令的非暂时性计算机可读存储介质,该指令在由处理器执行时使处理器进行如下操作:接收采用矩形格式的360度图像;拷贝该图像的第一边缘附近的图像部分;将所拷贝的图像部分粘贴在该图像的第二边缘附近,从而形成修改后的图像;以及利用至少一个神经网络来分析修改后的图像,以标识出现在修改后的图像中的人员的总数。该非暂时性计算机可读存储介质可以进一步存储指令,该指令在由处理器执行时进一步使处理器进行如下操作:利用至少一个神经网络来分析修改后的图像,以确定出现在修改后的图像中的人员的身份。
[0032]尽管已经在本文中说明和描述了特定示例,但是在不偏离本公开的范围的情况下,各种各样的可替代和/或等同的实现方式可以代替所示出和描述的特定示例。本申请意图覆盖本文中讨论的特定示例的任何适配或变型。因此,所意图的是,本公开仅由权利要求及其等同物所限制。