WO2024045229A1 - Procédé et appareil de blocage d'alerte précoce pour scénario spécial, et dispositif électronique et support de stockage - Google Patents

Procédé et appareil de blocage d'alerte précoce pour scénario spécial, et dispositif électronique et support de stockage Download PDF

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Publication number
WO2024045229A1
WO2024045229A1 PCT/CN2022/119597 CN2022119597W WO2024045229A1 WO 2024045229 A1 WO2024045229 A1 WO 2024045229A1 CN 2022119597 W CN2022119597 W CN 2022119597W WO 2024045229 A1 WO2024045229 A1 WO 2024045229A1
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Prior art keywords
identified
image
feature points
fire
special scene
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PCT/CN2022/119597
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English (en)
Chinese (zh)
Inventor
马燕娟
董志勇
熊艳
渠红海
王蕾
康谊广
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武汉理工光科股份有限公司
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Publication of WO2024045229A1 publication Critical patent/WO2024045229A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/10—Terrestrial scenes
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/46—Descriptors for shape, contour or point-related descriptors, e.g. scale invariant feature transform [SIFT] or bags of words [BoW]; Salient regional features
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/74—Image or video pattern matching; Proximity measures in feature spaces
    • G06V10/75—Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video features; Coarse-fine approaches, e.g. multi-scale approaches; using context analysis; Selection of dictionaries

Definitions

  • the invention relates to the technical field of fire early warning, and specifically relates to a special scene early warning shielding method, device, electronic equipment and storage medium.
  • Automatic fire alarm In the early stage of a fire, physical quantities such as smoke, heat, and light radiation generated by combustion are converted into electrical signals through fire detectors such as temperature, smoke, and light sensors, which are transmitted to the fire alarm controller and simultaneously display the occurrence of the fire. location and record the time when the fire occurred. Automatic fire alarm systems play a very important role in building fire prevention.
  • the purpose of the present invention is to overcome the above technical deficiencies, provide a special scene early warning shielding method, device, electronic equipment and storage medium, to solve the problem of frequent repeated receiving in special scenes such as fire operations, fire drills, and facility maintenance in the existing technology. Technical issues with the alarm.
  • the present invention provides a special scene early warning shielding method, which includes:
  • a preset scale-invariant feature transformation algorithm is used to determine the matching feature points between the fire image to be identified and the special scene reference image, and the fire to be identified is determined based on the matching feature points.
  • the fire image to be identified is a special scene image, and a fire warning shielding program is started.
  • using a preset scale-invariant feature transformation algorithm to determine the Hausdorff distance between the fire image to be identified and the special scene reference image includes:
  • the preset Gaussian differential function is used to determine the feature points to be identified in the fire image to be identified and the reference feature points of the special scene reference image, where the feature points to be identified and the reference feature points match each other. ;
  • determining the feature points to be identified of the fire image to be identified and the reference feature points of the special scene reference image based on a preset Gaussian differential function includes:
  • the feature points to be identified and the reference feature points that match each other are determined.
  • the Gaussian differential function can be expressed by the following formula:
  • ⁇ is a parameter related to size
  • x and y represent the coordinates of image information pixels
  • m and n are parameters related to feature points.
  • determining the bidirectional Hausdorff distance between the feature point to be identified and the reference feature point includes:
  • Traverse all feature points to be identified in the recognition set calculate the distance between the feature points to be identified and all feature points in the reference set, determine the corresponding first shortest distance, and construct a first of the plurality of first shortest distances. target set;
  • the larger of the first one-way Hausdorff distance and the second one-way Hausdorff distance is determined to be the two-way Hausdorff distance.
  • obtaining a special scene reference image includes:
  • the plurality of original images are constructed into a special scene reference image set.
  • the scale-invariant feature transformation algorithm determines the difference value between the fire image to be identified and the special scene reference image, and further includes determining the fire image to be identified. The difference value between each original image in the special scene reference image set.
  • the present invention also provides a special scene early warning shielding device, which includes:
  • the acquisition module is used to acquire fire images to be identified and special scene reference image sets
  • the Hausdorff distance determination module is used to use a preset scale-invariant feature transformation algorithm to determine the matching feature points between the fire image to be identified and the special scene reference image, and determine the matching feature points based on the matching features. point, determine the Hausdorff distance between the fire image to be identified and the special scene reference image;
  • a judgment module used to judge the relationship between the Hausdorff distance and the threshold
  • the target module determines that the fire image to be identified is a special scene image, and starts a fire early warning shielding program.
  • the present invention also provides an electronic device, including: a processor and a memory;
  • the memory stores a computer-readable program that can be executed by the processor
  • the present invention also provides a computer-readable storage medium that stores one or more programs, and the one or more programs can be executed by one or more processors to Implement the steps in the special scene early warning shielding method as described above.
  • the special scene early warning shielding method, device, electronic equipment and storage medium provided by the present invention first determine the special scenes that do not require fire alarm early warning, and collect images of the special scenes to form special scene reference images. And obtain the fire image to be identified, and then use the preset scale-invariant feature transformation algorithm to determine the matching feature points between the fire image to be identified and the special scene reference image, and determine the fire to be identified by matching the feature points The Hausdorff distance between the image and the special scene reference image. Finally, by comparing the relationship between the Hausdorff distance and the preset threshold, the similarity between the fire image to be identified and the special scene reference image is determined. , determine whether the actual scene corresponding to the image to be identified is a special scene through the size of the similarity.
  • the Hausdorff distance When the Hausdorff distance is less than the preset threshold, it indicates the similarity between the fire image to be identified and the special scene reference image.
  • Maximum, that is, the actual application scenario corresponding to the fire image to be recognized may be non-fire scenarios such as fire work and equipment maintenance, and then choose to start the shielding program so that the system no longer receives alarm information in this area.
  • Figure 1 is a flow chart of an embodiment of the special scene early warning shielding method provided by the present invention.
  • FIG. 2 is a flow chart of an embodiment of step S102 in the special scene early warning shielding method provided by the present invention
  • FIG. 3 is a flow chart of an embodiment of step S201 in the special scene early warning shielding method provided by the present invention.
  • Figure 4 is a flow chart of an embodiment of step S202 in the special scene early warning shielding method provided by the present invention.
  • Figure 5 is a schematic diagram of an embodiment of the special scene early warning shielding device provided by the present invention.
  • FIG. 6 is a schematic diagram of the operating environment of an embodiment of the electronic device provided by the present invention.
  • the invention relates to a special scene early warning shielding method, device, electronic equipment and storage medium, which can be applied to various industries and places, such as large warehouses, office buildings, shops, hotels, streets, etc., through the fire early warning system.
  • the environmental conditions can be monitored in real time to keep out possible hidden dangers; currently fire detectors have different parameters for detecting fires, mainly including: temperature sensing, light sensing, smoke sensing, gas and composite detectors, and by setting fire alarms
  • the system is connected with the public monitoring system to detect fires promptly and accurately and provide early warning, which can minimize losses.
  • fire warnings are not required. In these situations, if too frequent fire warnings occur, it will cause fatigue and reduce the vigilance of the staff. , is not conducive to the development of fire early warning work. Therefore, it is necessary to provide a special scene early warning shielding method, device, electronic equipment and storage medium to solve the above-mentioned problems.
  • the method, device, equipment or computer-readable storage medium involved in the present invention can be integrated with the above system or relatively independent.
  • Figure 1 is a flow chart of a special scene early warning shielding method provided by an embodiment of the present invention. Please refer to Figure 1.
  • the special scene early warning shielding method includes:
  • S102 Use a preset scale-invariant feature transformation algorithm to determine the matching feature points between the fire image to be identified and the special scene reference image, and determine the to-be-identified feature points based on the matching feature points. Hausdorff distance between the fire image and the reference image of the special scene;
  • special scenes that do not require fire alarm warning are first determined, images of the special scenes are collected, a special scene reference image is formed, and the fire image to be identified is obtained, and then the preset scale-invariant feature transformation is used
  • the algorithm determines the matching feature points between the fire image to be identified and the special scene reference image, and determines the Hausdorff distance between the fire image to be identified and the special scene reference image through the matching feature points, and finally compares The relationship between the Hausdorff distance and the preset threshold is used to determine the similarity between the fire image to be identified and the special scene reference image. Based on the similarity, it is determined whether the actual scene corresponding to the image to be identified is a special scene.
  • the Hausdorff distance when the Hausdorff distance is less than the preset threshold, it means that the similarity between the fire image to be identified and the special scene reference image is the greatest, that is, the actual application scenario corresponding to the fire image to be identified may be a fire.
  • the actual application scenario corresponding to the fire image to be identified may be a fire.
  • Using a preset scale-invariant feature transformation algorithm to determine matching feature points between the fire image to be identified and the special scene reference image includes:
  • S201 Use the preset Gaussian differential function to determine the feature points to be identified in the fire image to be identified and the reference feature points of the special scene reference image, where the feature points to be identified and the reference feature points are in pairs. match each other;
  • the SIFT algorithm i.e., scale-invariant feature transformation algorithm
  • the SIFT algorithm is used to extract the feature points to be identified of the fire image to be identified and the reference feature points of the special scene reference image, where the feature points to be identified are
  • the feature points on the fire image that best reflect the actual scene represented by the image, that is, the feature points to be identified can intuitively reflect the similarity between the fire image to be identified and the special scene reference image.
  • the reference feature points are on the special scene reference image.
  • Feature points that best reflect the actual scene represented by the image and calculate the two-way Hausdorff distance between the feature points to be identified and the reference feature points, and reflect the fire image and special scene to be identified through the Hausdorff distance
  • the similarity between the reference images is used to determine the fire situation at the fire location to be identified, thereby guiding the work of the fire alarm.
  • determining the feature points to be identified in the fire image to be identified and the reference feature points of the special scene reference image include:
  • S302. Determine the positions of the feature points to be identified and the reference feature points, as well as the direction of the feature to be identified and the direction of the reference feature;
  • S303 Construct a feature vector to be identified based on the position of the feature point to be identified and the direction of the feature to be identified;
  • the feature points to be identified and the reference feature points are extracted in order to search for image positions on all scale spaces, and to identify potential scale- and rotation-invariant interest points through a preset Gaussian differential function
  • the preset Gaussian differential function can be expressed by the following formula: Among them, ⁇ is a parameter related to size, x and y represent the coordinates of image information pixels, m and n are parameters related to feature points. For example, m and n can be randomly set parameter values, which are consistent with Gaussian differential function parameters. Define requirements.
  • a fine-fitting model is used to determine the position and scale of the feature point to be identified and the reference feature point, and then the position is assigned to each feature point based on the local gradient direction of the image. one or more directions;
  • the feature vector to be identified and the reference feature vector are constructed respectively. Finally, by comparing the feature points in the feature vector to be identified and the reference feature vector, a number of feature points to be identified that match each other are found. and reference feature points.
  • determining the bidirectional Hausdorff distance between the feature point to be identified and the reference feature point includes:
  • the recognition set and the reference set are respectively constructed based on the feature points to be recognized and the reference feature points that match each other in the feature vector to be recognized and the reference feature vector.
  • the feature points to be recognized in the recognition set are Points that can match the reference feature points.
  • the reference feature points in the reference set are points that can match the feature points to be identified.
  • the process of determining the bidirectional Hausdorff distance is for the following steps:
  • the longest distance between h(A, B) and h(B, A) is selected, which is the two-way Hausdorff distance of the set A and B.
  • obtaining a special scene reference image further includes:
  • the plurality of original images are constructed into a special scene reference image set.
  • the scale-invariant feature transformation algorithm determines the difference value between the fire image to be identified and the special scene reference image, and further includes determining the fire image to be identified. The difference value between each original image in the special scene reference image set.
  • the fire image to be identified and the special scene reference image set are compared for similarity until the most similar picture is found or the comparison is completed with all pictures.
  • the embodiment of the present invention also provides a special scene early warning shielding device 500.
  • the special scene early warning shielding device 500 includes an acquisition module 510 and a Hausdorff distance determination module 520. , judgment module 530 and target module 540.
  • the acquisition module 510 is used to acquire the fire image and special scene reference image set to be identified;
  • the Hausdorff distance determination module 520 is used to use a preset scale-invariant feature transformation algorithm to determine the matching feature points between the fire image to be identified and the special scene reference image, and based on the matching Feature points determine the Hausdorff distance between the fire image to be identified and the special scene reference image;
  • the judgment module 530 is used to judge the relationship between the Hausdorff distance and the threshold
  • Target module 540 determines that the fire image to be identified is a special scene image, and starts a fire warning shielding program.
  • the present invention also provides an electronic device.
  • the electronic device can be a mobile terminal, a desktop computer, a notebook, a palmtop computer, a server and other computing devices.
  • the electronic device includes a processor 610, a memory 620 and a display 630.
  • FIG. 6 only shows some components of the electronic device, but it should be understood that implementation of all the components shown is not required, and more or fewer components may be implemented instead.
  • the memory 620 may be an internal storage unit of the electronic device, such as a hard disk or memory of the electronic device. In other embodiments, the memory 620 may also be an external storage device of the electronic device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), or a secure digital (Secure Digital, SD) card equipped on the electronic device. Flash Card, etc. Further, the memory 620 may also include both an internal storage unit of the electronic device and an external storage device. The memory 620 is used to store application software and various data installed on the electronic device, such as program codes for installing the electronic device. The memory 620 may also be used to temporarily store data that has been output or is to be output. In one embodiment, a special scene early warning shielding program 640 is stored in the memory 620, and the special scene early warning shielding program 640 can be executed by the processor 610, thereby realizing the special scene early warning shielding method in each embodiment of the present application.
  • a special scene early warning shielding program 640 is stored in the memory
  • the processor 610 may be a central processing unit (CPU), a microprocessor or other data processing chip, used to run the program code stored in the memory 620 or process data, such as executing special scene warnings. Shielding methods, etc.
  • CPU central processing unit
  • microprocessor or other data processing chip
  • the display 630 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode, organic light-emitting diode) touch device, etc.
  • the display 630 is used to display information on the special scene early warning shielding device and to display a visual user interface.
  • the components of the electronic device 610-630 communicate with each other via the system bus.

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Abstract

La présente invention divulgue un procédé et un appareil de blocage d'alerte précoce pour un scénario spécial, ainsi qu'un dispositif électronique et un support de stockage. Le procédé consiste à : acquérir une image d'incendie à reconnaître et une image de référence de scénario spécial ; déterminer un point caractéristique correspondant entre ladite image d'incendie et l'image de référence du scénario spécial à l'aide d'un algorithme prédéfini de transformation de caractéristiques invariant à l'échelle, puis déterminer une distance de Hausdorff entre ladite image d'incendie et l'image de référence du scénario spécial en fonction du point caractéristique correspondant ; déterminer une relation de taille entre la distance de Hausdorff et une valeur seuil prédéfinie ; et si la distance de Hausdorff est inférieure à la valeur seuil prédéfinie, déterminer que ladite image d'incendie est une image de scénario spéciale, puis lancer un programme de blocage d'alerte précoce en cas d'incendie. La présente invention résout le problème technique de l'état de la technique selon lequel des alarmes répétées sont fréquemment reçues dans des scénarios spéciaux tels que le travail à chaud, les exercices incendie et la maintenance des installations.
PCT/CN2022/119597 2022-08-29 2022-09-19 Procédé et appareil de blocage d'alerte précoce pour scénario spécial, et dispositif électronique et support de stockage WO2024045229A1 (fr)

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Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103238159A (zh) * 2010-09-28 2013-08-07 华为技术有限公司 用于图像认证的系统和方法
US20200211221A1 (en) * 2018-12-26 2020-07-02 Hitachi, Ltd. Object recognition device and object recognition method
CN112446431A (zh) * 2020-11-27 2021-03-05 鹏城实验室 特征点提取与匹配方法、网络、设备及计算机存储介质
CN113486942A (zh) * 2021-06-30 2021-10-08 武汉理工光科股份有限公司 一种重复火警判定方法、装置、电子设备及存储介质
US20210319250A1 (en) * 2020-04-09 2021-10-14 Sensetime International Pte. Ltd. Matching method and apparatus, electronic device, computer-readable storage medium, and computer program
CN114119645A (zh) * 2021-11-25 2022-03-01 推想医疗科技股份有限公司 一种图像分割质量的确定方法、系统、设备及介质

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103238159A (zh) * 2010-09-28 2013-08-07 华为技术有限公司 用于图像认证的系统和方法
US20200211221A1 (en) * 2018-12-26 2020-07-02 Hitachi, Ltd. Object recognition device and object recognition method
US20210319250A1 (en) * 2020-04-09 2021-10-14 Sensetime International Pte. Ltd. Matching method and apparatus, electronic device, computer-readable storage medium, and computer program
CN112446431A (zh) * 2020-11-27 2021-03-05 鹏城实验室 特征点提取与匹配方法、网络、设备及计算机存储介质
CN113486942A (zh) * 2021-06-30 2021-10-08 武汉理工光科股份有限公司 一种重复火警判定方法、装置、电子设备及存储介质
CN114119645A (zh) * 2021-11-25 2022-03-01 推想医疗科技股份有限公司 一种图像分割质量的确定方法、系统、设备及介质

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
XIAOHAO: "The most Detailed Implementation of SIFT Algorithm Principle in the Entire Network", HUAWEI CLOUD, 24 March 2022 (2022-03-24), XP093144950, Retrieved from the Internet <URL:https://huaweicloud.csdn.net/63802f3edacf622b8df8643b.html> [retrieved on 20240325] *

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