Wearable mobile positioning image acquisition equipment-based intelligent body sensing method

Abstract

The invention discloses an intelligent body perception method based on wearable mobile positioning image acquisition equipment. The method comprises the steps of equipment wearing and mode setting, mobile positioning module behavior perception, image acquisition module environment perception, rear-end analysis module body perception modeling and visual platform integrated display. Based on wearable intelligent equipment, behavior information of an individual in a city space under a long time span is accurately tracked through a mobile positioning module, time-sharing space environment information is recorded through an image acquisition module, and an intelligent agent model is established through a back-end analysis module. The method can be used for personal perception of environment and behavior in the field of urban intelligent perception, realizes accurate identification of environment and behavior association features, and better serves urban decision making.

Description

Technical Field

[0001] The present invention belongs to the field of urban intelligent perception, and specifically relates to an embodied intelligent perception method based on a wearable mobile positioning image acquisition device. Background Art

[0002] There is a complex spatiotemporal interactive relationship between human behavior and the urban environment. The core goal of urban research and planning is to shape people's living environment and guide their lifestyle. Understanding the causal logic of behavior and spatial environment from an individual perspective can more accurately respond to people's diverse demands for urban space and optimize urban spatial layout. Traditional urban perception methods rely solely on spatial static image acquisition and manual spatial annotation. It is difficult to process the massive spatiotemporal information generated by multi-dimensional urban spatial elements and multiple behaviors, and it is even more difficult to effectively explain the complex characteristics and interactive relationships between behavior and environmental elements. On the other hand, the emergence of perception methods based on artificial intelligence technology provides a more efficient and scientific means of urban research.

[0003] At present, the behavior perception method level mostly uses instruments such as eye trackers, or monitors the behavior of people in a fixed space through cameras. The recognition content focuses on a single dimension, and it is difficult to accurately track the spatiotemporal behavior of individuals over a long time span. There is a lack of an embodied perception method that can provide high-frequency feedback, accurate matching, and can closely integrate complex human activities and conduct continuous tracking; in terms of perception content, it focuses more on the recognition of small indoor activities, and has not yet established a perception technology system for multiple behaviors in urban spaces of different scales. At the environmental perception method level, methods such as oblique photogrammetry and laser point cloud scanning are mostly used to collect spatial information and perform three-dimensional reconstruction. There is a lack of analysis of individual perception characteristics of space in combination with behavioral information, which has failed to form effective guidance for urban planning and design. Summary of the invention

[0004] Purpose of the invention: In view of the shortcomings of the prior art, the present invention provides an embodied intelligent perception method based on a wearable mobile positioning image acquisition device. The present invention is based on a wearable intelligent device, which can accurately track the dynamic and static behaviors of individuals in urban space, and identify synchronous spatial environmental characteristics, thereby realizing embodied intelligent perception and analytical modeling of behaviors and environments in cities.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] The embodied intelligent perception method based on a wearable mobile positioning image acquisition device includes the following steps:

[0007] (1) Device wearing and mode setting

[0008] The subjects are equipped with wearable components of the sensing device, including sensing glasses and smart bracelets. The mode adjustment system is used to adjust the device to the on state, in which the device can automatically switch the sensing mode and acquire and transmit multiple types of sensing data.

[0009] (2) Mobile positioning module behavior perception

[0010] The mobile positioning module includes a mobile positioning component and a behavior perception system, which automatically switches between dynamic perception mode and static perception mode according to the individual's speed change characteristics. In the dynamic perception mode, the mobile positioning component records the individual's GPS positioning data per unit time interval. In the static perception mode, the mobile positioning component records the instantaneous acceleration data of the individual's limbs. The behavior perception system analyzes and integrates dynamic behavior indicators and static behavior indicator information to form an individual behavior information library, and transmits real-time data to the data center through the cellular network.

[0011] (3) Image acquisition module environmental perception

[0012] The image acquisition module includes an image acquisition component and an environmental perception system. In dynamic perception mode, the image acquisition component acquires global spatial images. In static perception mode, the image acquisition component simultaneously acquires global spatial images and images of the visible range of the individual line of sight. The environmental perception system analyzes and integrates spatial environmental indicators and visual environmental indicators to form a spatial environmental information library, and transmits real-time data to the data center through a cellular network.

[0013] (4) Backend analysis module embodied perception modeling

[0014] The back-end analysis module includes server components, digital sandbox system, behavior modeling system and integrated analysis system. The digital sandbox system integrates spatial environment data and establishes a regional three-dimensional space sandbox through three-dimensional reconstruction methods. The behavior modeling system integrates individual behavior data, virtually reconstructs the spatiotemporal behavior of each subject, and maps the reconstructed virtual individual to the correct position of the three-dimensional space sandbox through its GPS positioning. The integrated analysis system comprehensively analyzes the parameters of individual behavior information and spatial environment information, and establishes an intelligent agent model based on the correlation characteristics of the parameters.

[0015] (5) Visualization platform integrated display

[0016] The data results output by the back-end analysis module are displayed through a holographic digital projector, gesture recognizer and visualization system, including the regional behavior environment reconstruction sandbox and the intelligent agent deduction sandbox.

[0017] Furthermore, the mobile positioning component in step 2 is composed of a GPS locator, a timer, and an inertial measurement unit, wherein the GPS locator obtains and records the real-time geographic information position of the individual, the timer records the time information, and the inertial measurement unit records the acceleration of the individual's limbs.

[0018] Furthermore, the step 2 of automatically switching the dynamic perception mode or the static perception mode according to the individual speed change characteristics refers to Calculate the individual speed standard deviation and speed change rate, and then calculate the speed fluctuation index S = α·σ v +β·△v, where α and β are weight coefficients. When the speed fluctuation index is greater than the threshold, it switches to the dynamic perception mode; when the speed fluctuation index is less than the threshold, it switches to the static perception mode.

[0019] Furthermore, the dynamic behavior index and static behavior index information in step 2 include the individual's speed and GPS coordinates, and the static behavior index information includes the posture time ratio and GPS coordinates. The posture includes standing, sitting and lying. The limb acceleration data recorded by the behavior perception system combined with the inertial measurement unit is obtained by Calculate the pitch and roll angles of the limbs, where (a x ,a y ,a z ) represents the acceleration components in the X, Y, and Z axis directions. The posture type of the individual is determined according to the threshold range of the pitch angle and the roll angle. Calculate the proportion of time spent in each posture.

[0020] Furthermore, the image acquisition component described in step three includes a panoramic camera and a local camera. The panoramic camera acquires 360° panoramic spatial images, and the local camera records spatial images of individual field of view at the perspective of human eye field of view.

[0021] Furthermore, the integrated spatial environment index and visual environment index information in step 3 refers to processing the spatial image collected by the image acquisition component through the semantic segmentation algorithm, identifying the green vegetation, water bodies, buildings, roads, sky, open space, and obstacle facilities in the panoramic spatial image, and calculating the number of pixels N occupied by each semantic i and through Calculate the proportion of each type of pixel as a spatial environment indicator. Identify green vegetation, water bodies, buildings, roads, open spaces and obstacle facilities in the visible range image, and similarly calculate the proportion of each type of semantic pixel as a visual environment indicator.

[0022] Furthermore, the step 4 comprehensively analyzes the individual behavior information and spatial environment information parameters, and establishes an intelligent perception and decision-making model based on the parameter association characteristics of the region, which means dividing the region into 10m*10m grid units and calculating the average behavior index B in each grid = [b1, b2, ..., b n ] and the spatial index average value E = [e1, e2, ..., e m ], the correlation model B = h(E) + ε between the behavior index and the space index is calculated through a neural network. The correlation model is embedded in the intelligent agent system, which receives the spatial environment index as input, outputs the individual behavior index and guides the behavior decision.

[0023] Beneficial effects:

[0024] 1. The present invention realizes accurate perception and coupling of individual actions and spatial environment through wearable devices. The wearable components of the device include sensing glasses and smart bracelets. The device is small in size and easy to wear, suitable for all kinds of people, laying the foundation for large-scale application in urban space research and planning; it has weak interference with the travel behavior and actions of the subjects, ensuring the effective control of experimental errors and realizing the accurate collection of action and environmental information.

[0025] 2. The present invention adapts to the two states of motion and stillness of people by intelligently switching the perception mode based on the calculation parameters of people's motion patterns in urban space. The motion perception mode focuses on collecting information on spatiotemporal trajectory and motion speed; the still perception mode focuses on collecting information on human posture. The combination of the two modes expands the content latitude of behavioral environment perception, comprehensively collects people's behavioral information, and effectively improves the efficiency of behavioral perception.

[0026] 3. The present invention supports the fine recognition of the individual's multiple behavioral postures in urban space through an inertial measurement unit, and judges the individual's fine posture based on the individual's limb acceleration, avoiding the problems of limited field of view and long time consumption in traditional posture perception methods that rely on image feature recognition.

[0027] 4. The present invention collects spatial images through panoramic cameras and local cameras to add embodied latitude information to spatial environment perception. The panoramic camera collects 360° panoramic images and calculates spatial environment indicators; the line of sight camera collects local spatial images from the perspective of the subject and calculates visual environment indicators.

[0028] 5. The present invention establishes a regional behavioral environment sandbox through virtual reconstruction of multi-device perception data, forming a behavior-environment analysis database for urban areas of different scales, providing a basis for the spatial and behavioral characteristics analysis of urban areas.

[0029] 6. The present invention establishes a parameter system of behavior and environmental perception and combines the correlation characteristics between the collected data analysis parameters to establish a correlation model, which is convenient for exploring the perception mode and behavioral patterns of the crowd on the urban environment.

[0030] 7. The present invention realizes the visualization expression and interactive feedback of perceived data through three-dimensional holographic projection technology. The three-dimensional images with high update frequency and high granularity enable planners and decision makers to understand the behavior-environment interaction relationship of the overall and local space of the area more clearly and effectively, providing scientific support for subsequent planning decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 is a flow chart of the method of the present invention;

[0032] Figure 2 It is a schematic diagram of individual behavior perception information;

[0033] Figure 3 It is a schematic diagram of spatial environment perception information. DETAILED DESCRIPTION

[0034] The following will be combined with the accompanying drawings of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0035] Example

[0036] like Figure 1-3 As shown, the technical solution of the present invention will be described in detail below by taking a community in the old city of a certain city as an example.

[0037] (1) Residents of different age groups in the community were selected as subjects, and the subjects were equipped with wearable components of the sensing device, including sensing glasses and smart bracelets. The mode adjustment system was used to adjust the device to the on state.

[0038] (2) The mobile positioning module automatically switches between dynamic perception mode and static perception mode according to the individual's speed change characteristics. In dynamic perception mode, the mobile positioning component records the individual's GPS positioning data at unit time intervals. In static perception mode, the mobile positioning component records the instantaneous acceleration data of the individual's limbs. The behavior perception system analyzes and integrates dynamic behavior indicators and static behavior indicator information to form an individual behavior information database, and transmits real-time data to the data center through the cellular network.

[0039] The mobile positioning component is composed of a GPS locator, a timer, and an inertial measurement unit, wherein the GPS locator obtains and records the real-time geographic information position of the individual, the timer records the time information, and the inertial measurement unit records the acceleration of the individual's limbs.

[0040] The automatic switching of the dynamic perception mode or the static perception mode according to the individual speed change characteristics refers to △v=vmin max Calculate the individual speed standard deviation and speed change rate, and then calculate the speed fluctuation index S = α·σ v +β·△v, where α and β are weight coefficients. When the speed fluctuation index is greater than the threshold, it switches to the dynamic perception mode; when the speed fluctuation index is less than the threshold, it switches to the static perception mode.

[0041] The dynamic behavior index and static behavior index information, the dynamic behavior index includes the speed and GPS coordinates of the individual, the static behavior index information includes the posture time ratio and GPS coordinates, the posture includes standing, sitting and lying, the limb acceleration data recorded by the behavior perception system combined with the inertial measurement unit, Calculate the pitch and roll angles of the limbs, where (a x ,a y ,a z ) represents the acceleration components in the X, Y, and Z axis directions. The posture type of the individual is determined according to the threshold range of the pitch angle and the roll angle. Calculate the proportion of time spent in each posture.

[0042] (3) Through the image acquisition module, the global spatial image is collected in the dynamic perception mode, and the global spatial image and the visible range image of the individual line of sight are collected simultaneously in the static perception mode. The spatial environment index and visual environment index information are analyzed and integrated through the environmental perception system to form a spatial environment information library, and the real-time data is transmitted to the data center through the cellular network.

[0043] The image acquisition component includes a panoramic camera and a local camera. The panoramic camera acquires 360° panoramic spatial images, and the local camera records spatial images within an individual field of view at the perspective of the human eye.

[0044] The integrated spatial environment index and visual environment index information refers to processing the spatial image collected by the image acquisition component through the semantic segmentation algorithm, identifying the green vegetation, water bodies, buildings, roads, sky, open space, and obstacle facilities in the panoramic spatial image, and calculating the number of pixels N occupied by each semantic i and through Calculate the proportion of each type of pixel as a spatial environment indicator. Identify green vegetation, water bodies, buildings, roads, open spaces and obstacle facilities in the visible range image, and similarly calculate the proportion of each type of semantic pixel as a visual environment indicator.

[0045] (4) Through the back-end analysis module, the digital sand table system integrates the spatial environment data and establishes a regional three-dimensional space sand table through the three-dimensional reconstruction method. The behavior modeling system integrates the individual behavior data, virtually reconstructs the spatiotemporal behavior of each subject, and maps the reconstructed virtual individual to the correct position of the three-dimensional space sand table through its GPS positioning. The integrated analysis system comprehensively analyzes the individual behavior information and spatial environment information parameters, and establishes an intelligent agent model based on the correlation characteristics of the parameters.

[0046] The comprehensive analysis of individual behavior information and spatial environment information parameters, and the establishment of an intelligent perception and decision-making model based on the parameter association characteristics of the region, refers to dividing the region into 10m*10m grid units, and calculating the average value of the behavior index in each grid B=[b1,b2,...,b n ] and the spatial index average value E = [e1, e2, ..., e m ], the correlation model B = h(E) + ε between the behavior index and the space index is calculated through a neural network. The correlation model is embedded in the intelligent agent system, which receives the spatial environment index as input, outputs the individual behavior index and guides the behavior decision.

[0047] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected.

Claims

1. An embodied intelligent perception method based on a wearable mobile positioning image acquisition device, characterized in that: The following steps are involved: Step 1: Wear the device and set the mode The subjects are provided with wearable components of the sensing device, including sensing glasses and a smart bracelet; the device is adjusted to the on state using the mode adjustment system, in which the device can realize automatic switching of sensing modes and acquisition and transmission of multiple types of sensing data; Step 2: Mobile positioning module behavior perception The mobile positioning module includes a mobile positioning component and a behavior perception system, which automatically switches between dynamic perception mode and static perception mode according to the speed change characteristics of the individual; in the dynamic perception mode, the mobile positioning component records the GPS positioning data of the individual at unit time intervals; in the static perception mode, the mobile positioning component records the instantaneous acceleration data of the individual's limbs. The behavior perception system analyzes and integrates dynamic behavior indicators and static behavior indicator information to form an individual behavior information database, and transmits real-time data to the data center through the cellular network; Step 3: Image acquisition module environmental perception The image acquisition module includes an image acquisition component and an environmental perception system. In the dynamic perception mode, the image acquisition component acquires global spatial images. In the static perception mode, the image acquisition component simultaneously acquires global spatial images and images of the visible range of the individual's line of sight. The environmental perception system analyzes and integrates spatial environmental indicators and visual environmental indicators to form a spatial environmental information library, and transmits real-time data to the data center through the cellular network. Step 4: Embodied Perception Modeling in the Backend Analysis Module The back-end analysis module includes server components, digital sandbox system, behavior modeling system and integrated analysis system; the digital sandbox system integrates spatial environment data and establishes a regional three-dimensional space sandbox through a three-dimensional reconstruction method; the behavior modeling system integrates individual behavior data, virtually reconstructs the spatiotemporal behavior of each subject, and maps the reconstructed virtual individual to the correct position of the three-dimensional space sandbox through its GPS positioning; the integrated analysis system comprehensively analyzes individual behavior information and spatial environment information parameters, and establishes an intelligent body model based on the correlation characteristics of the parameters; Step 5: Visualization platform integration display The data results output by the back-end analysis module are displayed through a holographic digital projector, gesture recognizer and visualization system, including the regional behavior environment reconstruction sandbox and the intelligent agent deduction sandbox.

2. The embodied intelligent perception method based on a wearable mobile positioning image acquisition device according to claim 1 is characterized in that: The mobile positioning component in step 2 is composed of a GPS locator, a timer, and an inertial measurement unit, wherein the GPS locator obtains and records the real-time geographic information position of the individual, the timer records the time information, and the inertial measurement unit records the acceleration of the individual's limbs.

3. The embodied intelligent perception method based on a wearable mobile positioning image acquisition device according to claim 2 is characterized in that: In the step 2, the dynamic perception mode or the static perception mode is automatically switched according to the individual speed change characteristics. △v=v max -v min Calculate the individual speed standard deviation and speed change rate, where v i is the speed in the ith time period, n is the number of time periods, is the average velocity, v max and v min It is the maximum speed and minimum speed in a certain period of time; and then calculate the speed fluctuation index S = α·σ v +β·△v, where α and β are weight coefficients. When the speed fluctuation index is greater than the threshold, it switches to the dynamic perception mode; when the speed fluctuation index is less than the threshold, it switches to the static perception mode.

4. The embodied intelligent perception method based on a wearable mobile positioning image acquisition device according to claim 3 is characterized in that: The dynamic behavior index and static behavior index information in step 2 include the speed and GPS coordinates of the individual, and the static behavior index information includes the posture time ratio and GPS coordinates. The posture includes standing, sitting and lying. The limb acceleration data recorded by the behavior perception system combined with the inertial measurement unit is obtained by Calculate the pitch and roll angles of the limbs, where (a x ,a y ,a z ) represents the acceleration components in the X, Y, and Z axis directions. The posture type of the individual is determined according to the threshold range of the pitch angle and the roll angle. Calculate the proportion of each posture time, where T total is the total time from the start of recording to the end of recording, is the sum of all different posture times.

5. The embodied intelligent perception method based on a wearable mobile positioning image acquisition device according to claim 4 is characterized in that: The image acquisition component in step three includes a panoramic camera and a local camera. The panoramic camera acquires a 360° panoramic spatial image, and the local camera records the spatial image of the individual field of view at the perspective of the human eye.

6. The embodied intelligent perception method based on a wearable mobile positioning image acquisition device according to claim 5 is characterized in that: The integration of spatial environment indicators and visual environment indicator information in step 3 refers to processing the spatial images collected by the image acquisition component through the semantic segmentation algorithm, identifying green vegetation, water bodies, buildings, roads, sky, open spaces, and obstacle facilities in the panoramic spatial image, and calculating the number of pixels N occupied by each semantic i and through Calculate the proportion of each type of pixels as a spatial environment indicator, where N tatal Represents the total number of pixels in the image; identifies green vegetation, water bodies, buildings, roads, open spaces and obstacle facilities in the visible range image, and similarly calculates the proportion of each type of semantic pixels as a visual environment indicator.

7. The embodied intelligent perception method based on a wearable mobile positioning image acquisition device according to claim 6 is characterized in that: In the step 4, the individual behavior information and the spatial environment information parameters are comprehensively analyzed, and an intelligent perception and decision model is established according to the parameter association characteristics of the region, which means dividing the region into 10m*10m grid units, and calculating the average value vector B of the behavior index in each grid. n ] and the spatial index average vector E = [e1, e2, ..., e m ], where b1, b2, ..., b n Represents n different behavior indicators in the first grid, e1, e2, ..., e m Representing m different spatial environment indicators within the grid, the correlation model B=f(E)+ε between the behavior indicator and the spatial indicator is calculated through a neural network, where ε is the error term; the correlation model is embedded in the intelligent agent system, which receives the spatial environment indicator as input, outputs the individual's behavior indicator and guides the behavior decision.

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