A method for associating and / or naming a camera of a security system according to a location

Abstract

A method for associating and / or naming a camera of a security system according to a location. The method comprises obtaining an image captured by the camera, analyzing the image to identify characteristics indicative of a location visible in the image, determining a semantic description based on the identified characteristics, and associating the camera with the location and / or naming the camera according to the location based on the 5 semantic description.

Description

[0001] 2025PF80005 1

[0002] A method for associating and / or naming a camera of a security system according to a location.

[0003] FIELD OF THE INVENTION

[0004] The invention relates to a method for associating and / or naming a camera of a security system according to a location. The invention further relates to a controller, a system and computer program product for associating and / or naming a camera of a security system according to a location.

[0005] BACKGROUND OF THE INVENTION

[0006] In modem security systems, cameras are widely used to monitor various locations for safety and surveillance purposes. These cameras are often installed in diverse environments, ranging from residential properties to commercial and industrial sites.

[0007] Effective management of these cameras is crucial for ensuring comprehensive coverage and quick response to incidents.

[0008] One of the challenges in managing security cameras is the identification and naming of each camera. Traditionally, this process has been manual, requiring significant time and effort from security personnel, installers or users. Furthermore, as the number of cameras in a security system increases, the complexity of managing these cameras also grows.

[0009] JP7505844B2 relates to a navigation system for a host vehicle including at least one processor programmed to receive from a camera onboard the host vehicle at least one captured image representative of an environment of the vehicle; detect a pedestrian represented in the at least one captured image; analyze the at least one captured image to determine an indicator of the pedestrian represented in the at least one captured image.

[0010] US2022 / 319183A1 relates to a system comprising a first database and a server arrangement that obtains data from a plurality of sensors arranged in a surveillance area. The server arrangement transforms source coordinates of an image plane from a first reference system to corresponding coordinates in a map plane in a second reference system for calculating a new geospatial location of the object to be tracked.2025PF80005 2

[0011] SUMMARY OF THE INVENTION

[0012] The inventors have realized that the manual process of associating and naming cameras in a security system is time-consuming and prone to errors. It is therefore an object to provide a method that automatically associates and / or assigns a name to a camera of a security system according to a location.

[0013] According to a first aspect, the object is achieved by a method for associating and / or naming a camera to facilitate commissioning the camera into a security system according to a location. The method comprises, in a pre-commissioning mode, obtaining an image captured by the camera, analyzing the image to identify characteristics indicative of a location visible in the image and determining a semantic description based on the identified characteristics. The method further comprises, in a commissioning mode, associating the camera with the location and / or naming the camera according to the location based on the semantic description. This is beneficial as it significantly reduces the time and effort required for camera setup and management. By automating the process, the method ensures consistent and accurate naming of cameras, which enhances the efficiency of monitoring and responding to incidents. Additionally, it minimizes human error, leading to a more reliable and effective security system.

[0014] The identified characteristics may comprise objects and / or events indicative of a location. The image is analyzed to detect specific objects (such as furniture, appliances, etc.) and / or events (such as activities or movements) that are characteristic of a particular location. These identified characteristics are then used to determine the semantic description and associate or name the camera accordingly. For example, objects like a refrigerator, stove, and sink, and activities such as cooking or washing dishes, are indicative of “kitchen” location.

[0015] Pre-commissioning mode refers to the initial setup state of a security camera, in which the device is installed, powered, and capable of capturing images, but not yet fully integrated into the operational security system (e.g., for active monitoring or alerting). This mode enables basic configuration, diagnostics, and location-based analysis (e.g., identifying visual characteristics of the environment), without triggering system-level responses. In contrast, commissioning mode is the transitional phase during which the camera undergoes full functional testing, network association with the connected security system, and semantic tagging based on contextual image analysis. This process facilitates accurate identification and formal integration of the camera into the system for coordinated operation.2025PF80005 3

[0016] The method may further comprise grouping (associating) the camera with at least one further device of the system, wherein the location associated with the camera matches the location associated with the at least one further device. By grouping the camera with other devices (such as a lighting device, a sensor, a second camera, etc.) that cover / share the same location, the security system enhances coordination by ensuring that all devices in a specific location can work together seamlessly and provides comprehensive coverage of the area, enhancing situational awareness and allowing for more informed decision-making in security operations. Lastly, it facilitates automated integration of new devices into the system. When a new camera or device is added, it can be automatically grouped with existing devices in the same location, reducing the need for manual configuration and ensuring a more efficient and user-friendly security system.

[0017] The method may further comprise visualizing, in a user interface, the camera to be in a same location as the at least one further device of the system (e.g., a second camera, a sensor, etc.). This visualization allows users to easily see and manage all devices associated with a particular location, improving situational awareness and facilitating quicker responses to incidents. It also simplifies the process of configuring and monitoring the system, as users can intuitively understand the spatial relationships between devices.

[0018] The method may further comprise associating and / or naming the camera according to a location from a set of predetermined locations. For example, the set of predetermined locations may be locations already associated with one or more further devices of the system. This ensures that the camera's location and name are consistent with the naming convention used for other devices in the system, facilitating easier management and configuration.

[0019] The method may further comprise outputting, e.g. in a user interface, the associated location and / or name to a user for approval. This step allows the user to review and confirm the automatically generated name and location association for the camera, ensuring accuracy and providing an opportunity for any necessary adjustments before finalizing the configuration.

[0020] The method may further comprise determining a plurality of semantic descriptions based on the identified characteristics and outputting the plurality of semantic descriptions to a user for approval, wherein optionally said semantic descriptions are ranked according to a level of prominence of the identified characteristics in the camera’s field of view. Level of prominence is indicative of the significance or visibility of the identified characteristics within the camera's field of view. Characteristics that are more prominent are2025PF80005 4

[0021] those that are more easily identifiable (e.g., are more centrally located within the camera’s field of view), occupy a larger portion of the image e.g., in number of pixels, or are more distinct compared to other elements in the scene. For example, in a camera view of a living room, a large sofa might be more prominent than a small decorative item on a shelf. The ranking of semantic descriptions based on prominence helps prioritize the most relevant and descriptive names for the camera's location.

[0022] The method may further comprise determining a plurality of semantic descriptions based on the identified characteristics and associating and / or naming the camera according to a location from the set of predetermined locations based on a semantic similarity between at least one semantic description from the plurality of semantic descriptions and a location from the set of predetermined locations. This involves generating multiple potential descriptions for the camera's location, evaluating the similarity between each description and the predefined locations, and selecting the description that meets or exceeds a certain similarity threshold.

[0023] The camera may be controlled according to a natural language instruction provided by a user based on the associated location and / or name of the camera. That is, the semantic description or name / location is stored and used to interpret user references to the camera in natural language. For example, a user may provide a voice command and / or a text instruction to address or to control the camera, such as "show me the living room camera" or "rotate the kitchen camera," leveraging the associated names and locations to facilitate interaction with the security system.

[0024] The method may further comprise associating the camera with the location and / or naming the camera according to the location based on signals from one or more further sensors of the system. For example, a motion sensor detecting movement in a specific area can help confirm the camera's location, or a temperature sensor indicating a kitchen environment can assist in accurately naming the camera as "kitchen camera" or “garden camera”. By integrating these additional sensor signals, the system can enhance the precision of the camera's location association and naming process.

[0025] The method may further comprise determining the presence of a further camera associated with and / or named according to the location, receiving a further image from the further camera, determining updated semantic descriptions for the camera and the further camera, wherein the updated semantic descriptions are based on a prominence level of the identified characteristics in the images, and updating the naming of the camera and the further camera based on their updated semantic descriptions. The updated semantic2025PF80005 5

[0026] descriptions are based on a prominence level of identified characteristics in the image and the further image. For example, if a first camera is present in the kitchen entrance previously named as “kitchen camera” and a second camera is being added to monitor the kitchen counter, the system determines that “kitchen” location is already used. The system will decide that both devices need more specific (more granular) naming. Therefore, it names the new camera "kitchen counter camera" and renames the first camera "kitchen entrance camera." This approach ensures that semantic descriptions are made more specific when multiple cameras are present in one location and that initial camera names are updated if additional cameras are added to the same location.

[0027] According to a second aspect, the object is achieved by a controller for associating and / or naming a camera to facilitate commissioning the camera into a security system according to a location. The controller is configured to obtain an image captured by the camera prior to commissioning, analyze the image to identify characteristics indicative of a location visible in the image, and determine a semantic description for the camera based on the identified characteristics. The controller is configured to associate the camera with the location and / or naming the camera according to the location based on the semantic description during commissioning.

[0028] According to a third aspect, the object is achieved by a security system, the security system comprising a camera and a controller according to the second aspect.

[0029] According to a fourth aspect, the object is achieved by a computer program product for a computing device, the computer program product comprising computer program code to perform the method of the first aspect when the computer program product is run on a processing unit of the computing device.

[0030] Moreover, a computer program for carrying out the methods described herein, as well as a non-transitory computer readable storage-medium storing the computer program are provided. A computer program may, for example, be downloaded by or uploaded to an existing device or be stored upon manufacturing of these systems.

[0031] As will be appreciated by one skilled in the art, aspects of the present invention may be embodied as a device, a method or a computer program product.

[0032] Accordingly, aspects of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, microcode, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a "circuit", "module" or "system." Functions described in this disclosure may be implemented as an algorithm executed by a processor / microprocessor2025PF80005 6

[0033] of a computer. Furthermore, aspects of the present invention may take the form of a computer program product embodied in one or more computer readable medium(s) having computer readable program code embodied, e.g., stored, thereon.

[0034] Any combination of one or more computer readable medium(s) may be utilized. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a computer readable storage medium may include, but are not limited to, the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of the present invention, a computer readable storage medium may be any tangible medium that can contain, or store, a program for use by or in connection with an instruction execution system, apparatus, or device.

[0035] A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

[0036] Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber, cable, RF, etc., or any suitable combination of the foregoing. Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java(TM), Smalltalk, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer, or entirely on the remote computer or server. In2025PF80005 7

[0037] the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0038] Aspects of the present invention are described below with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor, in particular a microprocessor or a central processing unit (CPU), of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer, other programmable data processing apparatus, or other devices create means for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0039] These computer program instructions may also be stored in a computer readable medium that can direct a computer, other programmable data processing apparatus, or other devices to function in a particular manner, such that the instructions stored in the computer readable medium produce an article of manufacture including instructions which implement the function / act specified in the flowchart and / or block diagram block or blocks.

[0040] The computer program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0041] The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of devices, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the blocks may occur out of the order noted in the figures. For example,2025PF80005 8

[0042] two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and / or flowchart illustrations, and combinations of blocks in the block diagrams and / or flowchart illustrations, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

[0043] BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The above, as well as additional objects, features and advantages of the disclosed systems, devices and methods will be better understood through the following illustrative and non-limiting detailed description of embodiments of devices and methods, with reference to the appended drawings, in which:

[0045] Fig. 1 shows schematically an example of a security system for monitoring an environment;

[0046] Fig. 2 shows schematically an example of a user interface of a security system; Fig. 3 shows schematically... ;

[0047] Fig. 4 shows schematically a method of associating and / or naming a camera of a security system according to a location.

[0048] All the figures are schematic, not necessarily to scale, and generally only show parts which are necessary in order to elucidate the invention, wherein other parts may be omitted or merely suggested.

[0049] DETAILED DESCRIPTION

[0050] Fig. 1 shows an example of a security system 100 for monitoring an environment 1 such a building, an apartment, etc. The security system 100 comprises a controller 106. When a new camera 40 is being installed in the security system, in a pre-commissioning mode, the controller 106 is configured to obtain an image 107 or a sequence of images (video) captured by the camera. The controller 106 is further configured to analyze the image to identify characteristics indicative of a location visible in the image. The identified characteristics may for example comprise objects and / or events indicative of a location. For example, the presence of cooking appliances and food preparation activities are indicative of a kitchen location. Similarly, detecting a pavement with a parked car is indicative of a driveway location. Object detection algorithms such as convolutional neural networks (CNNs) or other machine learning models may be used to detect and locate objects2025PF80005 9

[0051] within the image. For example, detecting a person, a stove, a bed, etc. Pose estimation techniques may be used to analyze posture and positions of people in the image. Activity recognition models may be used to analyze sequences of images and detect activities over time. Scene understanding techniques may be used to analyze the overall scene and its components. This involves recognizing the setting (e.g., kitchen, living room) and the typical activities associated with it. Typically, the image 107 is be pre-processed e.g., normalization, resizing, before said analysis. The controller 106 is configured to determine a semantic description (e.g., a semantic description associated with the image) based on the identified characteristics. For example, a classification layer, e.g., a softmax layer, may be used to classify the image into predefined categories (e.g., kitchen, backyard). In another example, a visual language model may be used to generate the semantic description based on the identified characteristics in the image.

[0052] During commissioning, in a commissioning mode or phase, the controller 106 is configured to associate the camera 40 with the location and / or name the camera according to the location based on the semantic description derived in the pre-commissioning phase.

[0053] For example, the controller 106 may reference a database of predetermined locations and matches the semantic description with the closest predetermined location. For example, the sematic description for an image 107 may be “People cooking in a kitchen” and the predetermined set of locations may be [“Bedroom”, “Bathroom”, “Living room”, “Kitchen”]. The controller 106 associates the camera with the “Kitchen” location and / or names the camera “Kitchen camera” for example based on semantic similarity between the sematic description and the predetermined locations. The set of predefined locations may comprise locations already associated with one or more further devices of the system. For example, a lighting system may be present in the environment 1. The lighting system comprises one or more lighting devices 30 - 34 already associated or named according to corresponding locations. For example, lighting device 30 is associated with a “living room” location, lighting device 32 is associated with a “kitchen” location and lighting device 34 is associated with a “backyard” location. In examples, the controller 106 may determine during the pre-commissioning phase one or more semantic descriptions for an image, e.g., “kids playing”, “kids in a living room”, “modem apartment”. The controller 106 may name or associate the camera with a location based on a semantic similarity between at least one of the sematic descriptions from the one or more semantic descriptions and a location from the set of predetermined locations. In this example, the controller 106 associates the camera with the “living room” location and / or names the camera “living room camera” based on semantic2025PF80005 10

[0054] similarity between “kids in a living room” description and the “living room” location. In another example, the semantic description may be “Bedroom” and one or more sensors of the system are named as “Tim’s bedroom”. The controller 106 associates the camera with the “Tim’s bedroom” location and / or names the camera as “Tim’s bedroom” based on semantic similarity. The database may be stored in a memory 108 communicatively coupled with the controller 106. Additionally, an / or alternatively, database may be stored in cloud 120 communicatively coupled with the controller 106.

[0055] Additionally, and or alternatively, if the locations are not predefined or the semantic similarity between the determined semantic description and the predetermined locations is below a threshold, the controller 106 may assign anew location based on the semantic description. For example, the controller may use predefined naming conventions to generate a name for the camera. For example, if the semantic description indicates a kitchen, the camera might be named "Kitchen Camera."

[0056] The controller 106 may store the corresponding association and / or name in a memory 108. The controller 106 may additionally and / or alternatively store the semantic description or plurality of semantic descriptions associated with the image in a memory 108 or cloud 120. The determined association and / or name may be visualized to the user in a user interface.

[0057] In embodiments, the controller 106 may be configured to group the camera 40 with at least one further device of the system if the location associated with the camera matches the location associated with the at least one further device. As an example, the camera 40 associated with a kitchen location is grouped (associated) with lighting device 32 which is also associated with the “kitchen” location. Fig. 2 shows an example of a user interface 200 where the camera 40 is visualized to be in a same location 20 as the lighting device 32 (e.g., the kitchen location 20 of the home environment 1).

[0058] The controller 106 may prompt the user to verify the assigned name and / or location. The user may confirm or adjust the location accordingly. The controller 106 may automatically adjust the association and / or naming if new data is provided by the user.

[0059] In embodiments, the controller 106 may determine, during the pre-commissioning phase, a plurality of semantic descriptions based on the identified characteristics. For example, the controller receives an image of a backyard with a swimming pool and based on the analysis determined the semantic descriptions: "backyard," "swimming pool," and "outdoor recreation area". The semantic descriptions may be ranked according to prominence of identified characteristics in the image. For example, the swimming pool2025PF80005 11

[0060] occupies a large portion of the image, giving it high prominence. The ranked semantic descriptions are presented to the user through a user interface as: "swimming pool,", "backyard," and "outdoor recreation area". The user reviews the descriptions and may approve the most accurate one. For example, the user might select " Swimming Pool" as the most fitting description.

[0061] In embodiments, the camera 40 may be controlled according to a natural language instruction provided by the user. For example, the user may use a voice assistant device, or a text / voice interface through an application to issue commands such as “rotate the backyard camera to the right”. The controller 106 may identify camera 40 as the backyard camera based on its associated location and / or name and control it accordingly.

[0062] The controller 106 may associate the camera 40 with the location and / or name the camera 40 according to the location based on signals from one or more further sensors of the system. For example, a motion sensor detecting movement in a specific area can help confirm the camera's location if movements are also detected in the camera feed, or a temperature sensor indicating a kitchen environment can assist in accurately naming the camera as "kitchen camera."

[0063] In certain cases, during the commissioning phase of camera 40, the controller 106 may determine the presence of a further (already commissioned) camera 42 that is associated with or named according to the same location. In the example of Fig.3, both cameras 40 and 42 are named as “Outdoor camera”. The controller 106 may be configured to receive a further image 110 from the further camera 42 and determine updated semantic descriptions for the camera 40 and the further camera 42. The updated semantic descriptions are based on a prominence level of the identified characteristics in images 107 and 110 received from cameras 40 and 42 respectively. In the example of Fig. 3, for camera 40 objects such as a fence, a bike storage place, etc. are prominent in image 307 received from camera 40. An updated more granular semantic description “backyard” is determined based on the identified characteristics. For camera 42, objects such as a door, a mat are prominent in image 310 received from camera 42. An updated more granular semantic description “front door” is determined based on the identified characteristics. Cameras 40 and 42 are consequently re-named as “backyard camera” and “front door camera” respectively based on the updated semantic descriptions.

[0064] Fig. 4 shows a method 400 for associating and / or naming a camera of a security system according to a location. The method comprising the steps of, during a pre-commissioning phase or mode, obtaining 402 an image captured by the camera, analyzing2025PF80005 12

[0065] 404 the image to identify characteristics indicative of a location visible in the image, and determining 406 a semantic description based on the identified characteristics. Preferably steps 402-406 may be performed locally on the camera. In embodiments, the camera may be connectively coupled with the cloud allowing at least part of the processing of steps 402-406 to be performed on the cloud. In embodiments, steps 402-406 may be performed on a bridge device, an edge server or edge gateway, the cloud or a combination thereof. In a commissioning phase or mode, the method 400 comprises associating 408 the camera with the location and / or naming the camera according to the location based on the semantic description. Step 408 may be performed locally on the camera, on a bridge device, an edge server or edge gateway, or the cloud.

[0066] The method 400 may be executed by computer program code of a computer program product when the computer program product is run on a processing unit of a computing device, such as the processor 106 of the security system 100.

[0067] Various embodiments of the invention may be implemented as a program product for use with a computer system, where the program(s) of the program product define functions of the embodiments (including the methods described herein). In one embodiment, the program(s) can be contained on a variety of non-transitory computer-readable storage media, where, as used herein, the expression “non-transitory computer readable storage media” comprises all computer-readable media, with the sole exception being a transitory, propagating signal. In another embodiment, the program(s) can be contained on a variety of transitory computer-readable storage media. Illustrative computer-readable storage media include, but are not limited to: (i) non- writable storage media (e.g., read-only memory devices within a computer such as CD-ROM disks readable by a CD-ROM drive, ROM chips or any type of solid-state non-volatile semiconductor memory) on which information is permanently stored; and (ii) writable storage media (e.g., flash memory, floppy disks within a diskette drive or hard-disk drive or any type of solid-state random-access semiconductor memory) on which alterable information is stored. The computer program may be run on the processor 302 described herein.

[0068] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and / or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or2025PF80005 13

[0069] addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0070] The corresponding structures, materials, acts, and equivalents of all means or step plus function elements in the claims below are intended to include any structure, material, or act for performing the function in combination with other claimed elements as specifically claimed. The description of embodiments of the present invention has been presented for purposes of illustration but is not intended to be exhaustive or limited to the implementations in the form disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the present invention. The embodiments were chosen and described to best explain the principles and some practical applications of the present invention, and to enable others of ordinary skill in the art to understand the present invention for various embodiments with various modifications as are suited to the particular use contemplated.

Claims

2025PF80005 14CLAIMS:

1. A method (400) for associating and / or naming a camera (40) to facilitate commissioning the camera into a security system according to a location, the method comprising the steps of:obtaining (402) an image (107) captured by the camera prior to commissioning;analyzing (404) the image to identify characteristics indicative of a location visible in the image;determining (406) a semantic description based on the identified characteristics;associating (408) the camera with the location and / or naming the camera according to the location based on the semantic description during commissioning.

2. The method of claim 1 further comprising:grouping the camera with at least one further device of the security system, wherein the location associated with the camera matches the location associated with the at least one further device.

3. The method of claim 2 comprising:visualizing, in a user interface, the camera to be in a same location as the at least one further device of the system.

4. The method according to any preceding claim, wherein the method comprises associating and / or naming the camera according to a location from a set of predetermined locations.

5. The method of claim 4, wherein the set of predefined locations comprises locations already associated with one or more further devices of the system.

6. The method according to any preceding claim further comprising:2025PF80005 15outputting the name and / or associated location to a user for approval.

7. The method according to any preceding claim further comprising:determining a plurality of semantic descriptions based on the identified characteristics and outputting the plurality of semantic descriptions to a user for approval; wherein said semantic descriptions are ranked according to a level of prominence of the identified characteristics in the image.

8. The method according to claim 4, wherein the method comprises:determining a plurality of semantic descriptions based on the identified characteristics;associating and / or naming the camera according to a location from the set of predetermined locations based on a semantic similarity between at least one semantic description from the plurality of semantic descriptions and a location from the set of predetermined locations.

9. The method according to any preceding claim wherein the camera is controlled according to a natural language instruction provided by a user based on the associated location and / or name of the camera.

10. The method according to any preceding claim wherein the step of associating the camera with the location and / or naming the camera is further based on signals from one or more further sensors of the system.

11. The method according to any preceding claim wherein the identified characteristics comprise objects and / or events indicative of a location.

12. The method according to any preceding claim wherein the method comprises:determining the presence of a further camera associated with and / or named according to the location;receiving a further image from the further camera;determining updated semantic descriptions for the camera and the further camera, wherein the updated semantic descriptions are based on a prominence level of identified characteristics in the images;2025PF80005 16updating the naming of the camera and / or the further camera based on the updated semantic descriptions.

13. A controller (106) for associating and / or naming a camera (40) to facilitate commissioning the camera into a security system (100) according to a location, the controller configured to:obtain an image captured by the camera prior to commissioning; analyze the image to identify characteristics indicative of a location visible in the image;- determine a semantic description based on the identified characteristics;associate the camera with the location and / or naming the camera according to the location based on the semantic description during commissioning.

14. A security system, the security system comprising a camera and a controller according to cl aim 13.

15. A computer program product for a computing device, the computer program product comprising computer program code to perform the method of claims 1-12 when the computer program product is run on a processing unit of the computing device.

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