A pedestrian tracking system based on behavior analysis

Abstract

The present invention discloses a pedestrian tracking system based on behavioral analysis in the field of video surveillance technology, comprising an image acquisition module for collecting crowd image information and environmental information; a cross-lens tracking module for achieving seamless identification and tracking of people; a behavior analysis module for analyzing and obtaining person motion data, human body attribute data, and crowd tracking data; a judgment module for judging a person's physical condition and predicting the cause of their injury or illness based on the person motion data and human body attribute data, and planning smooth movement routes within a hospital and predicting changes in crowd flow based on the crowd tracking data; a reporting module for alerting the hospital's emergency room based on the person's physical condition and providing the emergency doctor with the fastest route to the hospital; and a feedback module for optimizing the judgment of the person's physical condition and the cause of their injury or illness. The present invention is intelligent and efficient, increasing the survival rate of patients within a limited time, while also improving hospital management efficiency, safety, and service quality.

Description

Technical Field

[0001] The present invention belongs to the technical field of video surveillance, and in particular is a pedestrian tracking system based on behavior analysis. Background Art

[0002] With the continuous advancement of medical technology and the growing demand for healthcare, hospitals are facing increasing management pressure. Traditional hospital management often relies on manual record-keeping and monitoring, which is not only inefficient but also prone to errors. To improve hospital management efficiency, more intelligent and automated management methods are needed. Furthermore, as hospitals are places where patients are saved and treated, patient safety is of paramount importance. However, due to the complex hospital environment and frequent staff turnover, patients may encounter safety issues such as getting lost or falling during their treatment. Since the injured are themselves patients, they may require timely treatment and care after a fall, which places extremely high demands on the response and speed of emergency personnel.

[0003] While existing monitoring systems have a wide coverage area, they lack the ability to automatically alert emergency doctors when they detect an injured or sick person, nor can they track changes in hospital traffic density in real time to plan emergency routes. Therefore, it is necessary to propose a behavioral analysis-based pedestrian tracking system that can identify and analyze patient movements and body attributes, determine whether emergency care is needed, and alert emergency doctors. Furthermore, it can analyze traffic density and changing trends, provide intra-hospital route planning for emergency doctors and different patients, and improve hospital flow efficiency. Summary of the Invention

[0004] In order to solve the above problems, the purpose of the present invention is to provide a pedestrian tracking system based on behavioral analysis, which determines whether emergency treatment is needed by analyzing the patient's movements and human body attributes and reports to the emergency doctor. It has the function of planning the fastest arrival route for the emergency doctor, increasing the patient's survival rate within a limited time, while improving hospital management efficiency, safety and service quality.

[0005] To achieve the above-mentioned object, the technical solution of the present invention is as follows: a pedestrian tracking system based on behavior analysis, comprising an image acquisition module, a cross-lens tracking module, a behavior analysis module, a judgment module, a reporting module and a feedback module;

[0006] Image acquisition module, used to collect image information and environmental information of people within the hospital campus;

[0007] Cross-camera tracking module, which is used to achieve seamless recognition and tracking of people between different cameras based on cross-camera tracking technology;

[0008] The behavior analysis module is used to analyze the crowd image information and environmental information collected by the image acquisition module to obtain character movement data, human attribute data and crowd tracking data;

[0009] The judgment module is used to judge the physical condition of the person and predict the cause of the person's injury or illness based on the person's motion data and body attribute data. It also plans smooth movement routes within the hospital and predicts changes in the flow of people based on the crowd tracking data.

[0010] The reporting module is used to alert the hospital emergency room based on the person's physical condition and possible injuries. It also provides emergency doctors with the fastest arrival route based on the hospital's clear routes for priority rescue. It also notifies hospital visitors of changes in the flow of people to various departments based on the current clear routes and predicted changes in the flow of people within the hospital.

[0011] The feedback module is used to feed back the medical information of emergency patients to the judgment module, and combine the character's motion data and human body attribute data to refine the character's physical condition and the cause of the character's injury or illness.

[0012] The basic solution works as follows: the image acquisition module collects crowd image information and then uses cross-camera tracking technology to seamlessly identify and track people; the behavior analysis module analyzes people's movements and body attributes, as well as crowd tracking data; the judgment module determines whether emergency treatment is needed; and the reporting module reports the need for emergency doctors to rescue patients.

[0013] The basic solution offers the following benefits: 1. The image acquisition module and cross-camera tracking module capture crowd movements within the hospital in real time. Upon detecting a possible emergency situation, such as a fall or sudden collapse, the system immediately triggers an alarm and quickly notifies the emergency room via the reporting module. The judgment module uses crowd tracking data to map out unobstructed routes within the hospital and predict changes in crowd flow, providing emergency doctors with recommended routes to reach patients the fastest, significantly reducing emergency response time.

[0014] 2. By monitoring and analyzing traffic data in real time, the system can predict traffic trends within the hospital and provide decision-making support to hospital managers, such as adjusting registration windows and adding guidance services to alleviate congestion during peak hours. Based on traffic flow within each department and patient needs, the system can assist hospital managers in dynamically adjusting the allocation of medical resources, such as increasing physician shifts and deploying medical equipment, to ensure the efficient use of medical resources.

[0015] 3. The reporting module can recommend optimal routes to various departments based on current hospital traffic flow and traffic forecasts, reducing patient wait times and inconvenience during medical treatment. The behavioral analysis module not only identifies abnormal behavior but also analyzes the patient's physical condition and predicts the cause of injury or illness.

[0016] 4. The system can monitor abnormal situations within the hospital in real time, such as crowds and unusual behavior, and issue timely warnings to help hospitals strengthen safety management and prevent safety accidents. With data support from the behavior analysis module and the judgment module, hospital managers can more accurately grasp the hospital's safety status, providing a scientific basis for formulating and adjusting safety management measures.

[0017] 5. By collecting and analyzing large amounts of data, the system provides data-driven decision-making support for hospital managers, driving hospital management towards more intelligent and refined processes. As a key component of intelligent healthcare development, the system not only improves hospital operational efficiency and service quality but also provides valuable practical experience and data support for medical technology innovation.

[0018] Furthermore, the image acquisition module includes several surveillance cameras and several environmental sensors;

[0019] Several surveillance cameras, including several indoor cameras installed in the hospital's outpatient hall and several outdoor cameras installed in the hospital campus, are used to collect image information of people throughout the hospital and in secluded corners;

[0020] Several environmental sensors, including infrared cameras, temperature, humidity, and light sensors, are installed along with surveillance cameras to collect environmental information at different locations within the hospital.

[0021] The basic solution offers the following benefits: 1. By installing indoor and outdoor cameras in the hospital's outpatient lobby and campus, comprehensive monitoring is achieved throughout the entire hospital area, ensuring comprehensive safety. Indoor cameras clearly capture images of people in key areas like the outpatient lobby, while outdoor cameras monitor every corner of the campus, including secluded areas, enhancing monitoring accuracy and detail.

[0022] 2. The addition of environmental sensors such as infrared cameras, temperature, humidity, and light sensors enables the system to simultaneously collect environmental information from different locations within the hospital, including temperature, humidity, light intensity, and human infrared radiation, providing more comprehensive data support for hospital environmental monitoring. This environmental data can be used to intelligently adjust environmental conditions within the hospital, such as automatically adjusting air conditioning temperature and humidity, and turning lighting on and off, to create a more comfortable and safe treatment environment.

[0023] 3. By combining data from surveillance cameras and environmental sensors, the system can more accurately identify abnormal behaviors, such as people suddenly falling to the ground or abnormally high ambient temperature, and issue early warnings in a timely manner, providing strong support for hospital safety management.

[0024] 4. By real-time monitoring of environmental conditions within the hospital, the system can promptly detect and address potential risks that may lead to infection, such as bacterial growth caused by excessive humidity, thereby reducing the risk of hospital-acquired infections.

[0025] Furthermore, the cross-shot tracking module includes an image pre-processing unit, a person recognition unit, and a cross-shot matching unit;

[0026] The image preprocessing unit preprocesses crowd image information, including denoising, enhancement, and correction, and uses image registration technology to spatially align images collected by different cameras;

[0027] The person recognition unit is used to extract and identify the features of people in the crowd image information to establish a unique identification of the target;

[0028] The cross-shot matching unit is used to establish associations between different cameras based on the feature identifiers of the characters to achieve cross-shot matching of the target.

[0029] The basic solution offers the following benefits: 1. The image preprocessing unit denoises, enhances, and corrects the collected crowd image information, effectively improving the quality of the surveillance video. By removing noise, interference factors in the video are reduced; image enhancement improves image clarity and contrast, making human features more distinct; and image correction and registration techniques enable spatial alignment of images captured by different cameras, providing a foundation for subsequent cross-camera tracking.

[0030] 2. The person recognition unit uses advanced algorithms to extract and identify features of people in crowd images, establishing a unique identifier for the target. These features can include facial features, body contours, gait characteristics, etc. Through feature matching algorithms, the system can accurately identify the same person in different camera images, improving the accuracy of person recognition.

[0031] 3. The cross-camera matching unit establishes associations between different cameras based on the person's signature, enabling cross-camera matching of the target. This function enables the system to seamlessly track the target person across different cameras, continuously tracking and recording their movements regardless of their movement within the hospital.

[0032] Furthermore, the behavior analysis module includes a skeleton point analysis unit, a character attribute analysis unit, and a crowd flow analysis unit;

[0033] Skeleton point analysis unit, used to detect the temporal change motion data of a single human body and a single human body key point based on skeleton key point analysis technology;

[0034] A person attribute analysis unit, used to detect person body surface attribute data and identify person action data based on person ID classification detection technology;

[0035] The crowd analysis unit is used to count and track people based on deduplication crowd statistics technology.

[0036] The basic solution offers the following benefits: 1. The skeletal point analysis unit, based on skeletal key point analysis technology, can accurately detect key skeletal points within a single person and further analyze the temporal changes in these key points to accurately identify motion data. This technology is crucial for analyzing a patient's movement status and gait characteristics, helping doctors diagnose and treat diseases. By monitoring and analyzing skeletal point data in real time, the system can promptly detect and warn of abnormal behavior, such as falls and sudden acceleration, providing strong support for hospital safety management.

[0037] 2. The person attribute analysis unit, based on person ID classification detection technology, detects and identifies human surface attribute data and human motion data, such as height, body shape, clothing, and facial expressions. This data helps hospitals personalize patient identification and management, improving service quality and efficiency. By analyzing a patient's body surface attribute and motion data, the system can provide doctors with auxiliary diagnostic information, such as whether the patient's gait is abnormal or whether their expression is painful. This helps doctors more accurately diagnose the patient's condition and provides strong data support for subsequent judgment modules.

[0038] 3. The Crowd Flow Analysis Unit uses deduplication counting technology to accurately count the number of people flowing through the hospital and track their movements. This data helps hospital managers understand the flow of people within the hospital, optimize the allocation of medical resources, and improve service efficiency.

[0039] Furthermore, the judgment module includes a character situation judgment unit and a route planning unit;

[0040] A character condition judgment unit is used to judge whether the character needs first aid and possible symptoms of injury or illness based on the character's motion data and body attribute data;

[0041] The route planning unit is used to plan smooth movement routes within the hospital based on the crowd tracking data and the preset hospital route map.

[0042] The basic solution offers the following benefits: 1. The person's condition assessment unit can quickly determine whether a person requires emergency treatment based on their motion and body attribute data. For example, by analyzing parameters such as their gait, facial expression, and heart rate, the system can promptly detect any abnormalities in the patient, such as falls, coma, or abnormal heart rate, triggering an emergency response mechanism and buying valuable time for treatment.

[0043] 2. This unit can also further analyze the patient's possible injuries and illnesses. By comparing historical data with a medical knowledge base, the system can make a preliminary classification and assessment of the patient's injuries and illnesses, providing doctors with auxiliary preliminary diagnostic information, helping them to more accurately understand the patient's condition and develop more effective treatment plans.

[0044] 3. The route planning unit can plan smooth movement routes within the hospital in real time based on crowd tracking data and pre-set hospital route maps. By dynamically adjusting routes, the system can effectively avoid crowd congestion and patient delays, improve internal hospital traffic efficiency, and provide patients with a better medical experience.

[0045] 4. The route planning unit can also provide personalized navigation services for patients. By combining the patient's location information and destination, the system can plan the optimal route for them and update road conditions in real time to help patients quickly find their target department or facility.

[0046] Furthermore, the reporting module includes an alarm unit, a push unit, and a terminal application;

[0047] The alarm unit is used to alert the emergency doctor based on the first aid judgment result and possible injury symptoms of the person, and to plan the fastest arrival route for the emergency doctor based on the unobstructed movement routes within the hospital and provide prompts;

[0048] The push unit is used to push real-time traffic flow changes on the routes to various departments to hospital visitors based on the unobstructed movement routes and traffic flow change predictions within the hospital;

[0049] Terminal applications are used to prompt and promote smooth routes and real-time changes in pedestrian flow along these routes, and to display spatial pedestrian density distribution and temporal trends in pedestrian flow.

[0050] The basic solution offers the following benefits: 1. The alarm unit can immediately send an alert to emergency physicians based on the patient's first aid assessment and any potential injuries or illnesses. This ensures that emergency physicians are immediately notified of emergencies and can take swift action. The unit also plans the fastest route for emergency physicians based on clear routes within the hospital and provides real-time alerts. This helps emergency physicians quickly find the optimal route in emergencies, shortening response times and improving emergency care efficiency. Through instant alerts and optimized route planning, the alarm unit significantly improves medical safety. In emergencies, it ensures that patients receive timely and effective treatment, reducing medical risks and adverse consequences.

[0051] 2. The push unit can push real-time crowd flow change information on the routes to various departments to hospital visitors based on the smooth movement routes and crowd flow change predictions within the hospital. This helps visitors choose the best route and avoid crowd congestion and waiting time. By providing personalized route planning and real-time crowd flow change information, the push unit can significantly improve the medical experience of hospital visitors. Visitors can find their target department more conveniently, reducing their stay time and inconvenience in the hospital. Pushing real-time crowd flow change information helps hospital managers better understand the resource utilization within the hospital. By monitoring and analyzing crowd flow data, hospitals can optimize department layouts, adjust medical staff allocation, etc., thereby improving resource utilization efficiency and service quality.

[0052] 3. The terminal application can intuitively display spatial crowd density distribution and temporal crowd flow trends. This helps hospital managers and visitors better understand internal hospital traffic conditions, enabling them to make more informed decisions and choices. Through the terminal application, users can access unobstructed movement routes and real-time crowd flow information within the hospital anytime and anywhere. This provides significant convenience for users, reducing the inconvenience of searching for departments and waiting times within the hospital. The terminal application also provides user interaction features such as navigation guidance and route planning suggestions. This helps enhance interaction and communication between users and the hospital, improving user satisfaction.

[0053] Furthermore, the character attribute analysis unit includes human surface color block analysis, facial emotion analysis and character action recognition.

[0054] Human body surface color analysis, used to analyze clothing color, stains and bloodstains on the human body surface;

[0055] Facial emotion analysis, used to analyze emotional tendencies based on facial expressions;

[0056] Human action recognition, used to identify human actions.

[0057] The basic solution offers the following benefits: 1. Surface color analysis accurately identifies and tracks clothing color, which is particularly important in surveillance environments. By identifying clothing color, the system can more easily lock onto the target individual, achieving precise tracking and positioning. This feature can also detect stains and blood on the human body, which is crucial for scenarios such as medical emergencies and criminal investigations.

[0058] 2. Human motion recognition accurately identifies and classifies human movements, such as walking, running, and covering a wound. This helps the system analyze behavioral patterns, determining whether an individual is injured or sick, and the general type of injury. By monitoring human motion in real time, the system can promptly detect and issue warnings for abnormal behavior, such as falls and fighting. This helps reduce safety risks and ensure personal safety.

[0059] Furthermore, the crowd flow analysis unit includes multi-dimensional crowd flow analysis, crowd flow attribute analysis and crowd flow trajectory tracking.

[0060] Multi-dimensional crowd flow analysis is used to analyze the spatial dimension of crowd density distribution, record historical crowd flow data, and calculate the trend of crowd flow changes in the time dimension based on the preset three-dimensional map of the hospital;

[0061] Crowd attribute analysis, used to combine with the person attribute analysis unit to analyze the gender, age, and facial expression of the crowd, providing hospitals with refined crowd portraits;

[0062] Crowd flow tracking is used to track the flow of people and predict the real-time trend of crowd density changes.

[0063] The beneficial effects of the basic solution are: 1. Multi-dimensional crowd flow analysis can display the crowd density distribution in the spatial dimension in real time based on the preset three-dimensional map of the hospital. This helps hospital managers to intuitively understand the crowd flow conditions in each area, promptly identify congested areas, and take corresponding diversion measures. This function can also record and analyze historical crowd flow data, including peak and trough periods. By comparing crowd flow data in different time periods, hospitals can optimize resource allocation, such as adjusting medical staff schedules, adding service windows, etc., to improve service efficiency. Multi-dimensional crowd flow analysis can also count the crowd flow change trends in the time dimension, such as daily crowd flow, weekly crowd flow, monthly crowd flow, etc. Through trend prediction, hospitals can make preparations for personnel deployment, material reserves, etc. in advance to cope with possible peaks in crowd flow.

[0064] 2. Crowd attribute analysis can be combined with the person attribute analysis unit to conduct detailed analysis of crowd attributes such as gender, age, and facial expression. This helps hospitals build refined crowd profiles, understand the needs and preferences of different groups, and provide a basis for developing personalized service strategies. By understanding the needs and preferences of different groups, hospitals can provide targeted services, such as setting up children's play areas and rest areas for the elderly, to improve service quality and patient satisfaction. Refined crowd profiles can also support hospitals' market research and decision-making. For example, hospitals can adjust department layouts and optimize service processes based on the results of crowd attribute analysis to improve market competitiveness.

[0065] 3. Crowd tracking can track the flow of people in real time and predict changing trends in crowd density. This helps hospitals promptly identify potential congested areas and implement appropriate diversion measures to ensure smooth traffic flow within the hospital. By analyzing crowd flow trajectories, hospitals can understand patients' medical habits and preferences, thereby optimizing the medical process. For example, hospitals can adjust department layouts, add guidance services, etc. to shorten patient waiting times and medical processes. In emergency situations such as fires and earthquakes, crowd tracking can quickly locate people and provide critical information for emergency response. This helps hospitals evacuate people in a timely manner and reduce casualties.

[0066] Furthermore, the character situation judgment unit includes first aid judgment and injury refinement,

[0067] First aid judgment, which uses deep learning algorithms to train models to determine whether a person has symptoms of injury or illness and whether first aid is needed;

[0068] Injury refinement is used to determine the possible types of injuries a character may have based on their motion data and body attribute data.

[0069] The beneficial effects of the basic solution are as follows: 1. Using deep learning algorithms, the first aid judgment module can quickly analyze the patient's condition, determine whether there are any symptoms of injury or illness, and whether immediate first aid is required. This rapid response capability is crucial to saving lives, especially in emergency situations. By training the model, the first aid judgment module can learn and recognize a variety of injury and illness symptoms, thereby improving the accuracy of its judgment. This helps reduce misdiagnosis and missed diagnoses, ensuring that patients receive timely and correct treatment. The first aid judgment module can reasonably allocate medical resources based on the judgment results. For patients requiring first aid, the system can prioritize rescue personnel and medical equipment to ensure that patients receive timely treatment. At the same time, for patients who do not require first aid, the system can guide them to other appropriate treatments to avoid wasting medical resources.

[0070] 2. The injury and illness refinement module can determine the type of injury or illness a person may have based on their motion and body attribute data. This precise diagnostic capability helps doctors develop personalized first aid plans and improve treatment effectiveness.

[0071] Furthermore, terminal applications include personal terminals and hospital terminals.

[0072] Personal terminals are used to push smooth routes or the fastest arrival routes;

[0073] Hospital terminals are used to display multi-dimensional crowd flow analysis data.

[0074] The basic solution offers the following benefits: 1. The personal terminal intelligently recommends smooth routes or the fastest arrival routes based on the user's real-time location and destination, combined with backend crowd analysis data. This saves users time and effort, especially in crowded areas, effectively avoiding congestion and improving admission efficiency. By updating route information in real time, the personal terminal ensures users always have the latest road and pedestrian information. This dynamic information service not only enhances the user's travel experience but also strengthens their trust and satisfaction with the system.

[0075] 2. Hospital terminals can display multi-dimensional crowd flow analysis data in real time, including crowd density, speed, and direction. This data, presented in visual formats such as charts and maps, allows hospital managers to intuitively understand internal hospital crowd flow conditions and support decision-making. By analyzing crowd flow data, hospital managers can understand the distribution of traffic within each department and area, thereby rationally allocating medical resources, such as adding service windows and adjusting medical staff schedules. This helps improve hospital service efficiency, reduce patient wait times, and increase patient satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 Schematic diagram of a pedestrian tracking system based on behavior analysis in an embodiment of the present invention.

[0077] Figure 2 Schematic diagram of an image acquisition module of a pedestrian tracking system based on behavior analysis in an embodiment of the present invention.

[0078] Figure 3 Schematic diagram of a cross-shot tracking module of a pedestrian tracking system based on behavior analysis in an embodiment of the present invention.

[0079] Figure 4 Schematic diagram of a behavior analysis module and a judgment module of a pedestrian tracking system based on behavior analysis in an embodiment of the present invention.

[0080] Figure 5 Schematic diagram of a reporting module of a pedestrian tracking system based on behavior analysis in an embodiment of the present invention.

[0081] Figure 6 Schematic diagram of a person attribute analysis unit of a pedestrian tracking system based on behavior analysis in an embodiment of the present invention

[0082] Figure 7 Schematic diagram of a pedestrian flow analysis unit of a pedestrian tracking system based on behavior analysis in an embodiment of the present invention

[0083] Figure 8 Schematic diagram of a person situation determination unit of a pedestrian tracking system based on behavior analysis in an embodiment of the present invention DETAILED DESCRIPTION

[0084] The following is further described in detail through specific implementation methods:

[0085] Example 1

[0086] Basically as attached Figure 1 、 Figure 2 、 Figure 3 、 Figure 4 、 Figure 5 、 Figure 6 、 Figure 7 and Figure 8 Shown: A pedestrian tracking system based on behavior analysis, characterized by: including an image acquisition module, a cross-lens tracking module, a behavior analysis module, a judgment module, a reporting module and a feedback module.

[0087] The image acquisition module is used to collect image information of the crowd and environmental information within the hospital campus; the image acquisition module includes several surveillance cameras and several environmental sensors; the several surveillance cameras include several indoor cameras installed in the outpatient hall inside the hospital and several outdoor cameras installed in the hospital campus, which are used to collect image information of the crowd in the entire hospital and image information of the crowd in secluded corners; the several environmental sensors include infrared cameras, temperature, humidity and light sensors installed together with the surveillance cameras, which are used to collect environmental information at different locations in the hospital.

[0088] The cross-shot tracking module is used to achieve seamless recognition and tracking of people between different cameras based on cross-shot tracking technology; the cross-shot tracking module includes an image preprocessing unit, a person recognition unit and a cross-shot matching unit; the image preprocessing unit preprocesses the crowd image information, including denoising, enhancement and correction, and spatially aligns the images collected by different cameras through image registration technology; the person recognition unit is used to extract and identify the features of people in the crowd image information to establish a unique identification of the target; the cross-shot matching unit is used to establish associations between different cameras based on the feature identification of the people to achieve cross-shot matching of the target.

[0089] The behavior analysis module is used to analyze the crowd image information and environmental information collected by the image acquisition module to obtain character motion data, human attribute data and crowd tracking data; the behavior analysis module includes a skeleton point analysis unit, a character attribute analysis unit and a crowd analysis unit; the skeleton point analysis unit is used to detect the time-series change motion data of a single human body and a single human key point based on the skeleton key point analysis technology; the character attribute analysis unit is used to detect the character body surface attribute data and identify the character motion data based on the character ID classification detection technology. The character attribute analysis unit includes human surface color block analysis, facial emotion analysis and character motion recognition. The human surface color block analysis is used to analyze the color of clothing on the human body, dirt, etc. Stains and bloodstains, facial emotion analysis is used to analyze emotional tendencies based on character expressions, and character action recognition is used to identify character actions; the crowd analysis unit is used to count and track crowds based on deduplication crowd statistics technology. The crowd analysis unit includes multi-dimensional crowd analysis, crowd attribute analysis and crowd trajectory tracking. The multi-dimensional crowd analysis is used to analyze the spatial dimension crowd density distribution, record historical crowd data, and count the time dimension crowd change trend based on the preset three-dimensional hospital map. The crowd attribute analysis is used to combine the character attribute analysis unit to analyze the gender, age, and facial expressions of the crowd to provide the hospital with a refined crowd portrait. The crowd trajectory tracking is used to track the flow trajectory of the crowd and predict the real-time crowd density change trend.

[0090] The judgment module is used to judge the physical condition of the person and predict the cause of the person's injury or illness based on the person's motion data and human body attribute data, plan a smooth route of movement in the hospital and predict changes in crowd flow based on the crowd tracking data; the judgment module includes a person situation judgment unit and a route planning unit; the person situation judgment unit is used to judge whether the person needs first aid and possible symptoms of injury or illness based on the person's motion data and human body attribute data. The person situation judgment unit includes first aid judgment and injury refinement. First aid judgment is used to use a deep learning algorithm to train a model to judge whether the person has symptoms of injury or illness and whether first aid is needed. Injury refinement is used to judge the possible type of injury or illness of the person based on the person's motion data and human body attribute data; the route planning unit is used to plan a smooth route of movement in the hospital based on the crowd tracking data combined with a preset hospital route map.

[0091] The reporting module is used to alert the hospital emergency room based on the person's physical condition and possible injuries and illnesses, and based on the purpose of priority rescue, it prompts the emergency doctor the fastest arrival route based on the unobstructed movement routes in the hospital, and pushes the flow of people changes to the routes of various departments to hospital visitors based on the current unobstructed movement routes in the hospital and the prediction of crowd changes; the reporting module includes an alarm unit and a terminal application; the alarm unit is used to alert the emergency doctor based on the person's first aid judgment results and possible injury symptoms, and plan the fastest arrival route for the emergency doctor based on the unobstructed movement routes in the hospital and give prompts; the terminal application is used to prompt and promote unobstructed movement routes and real-time crowd changes on routes, and display spatial crowd density distribution and temporal crowd change trends. The terminal application includes a hospital terminal, and the hospital terminal is used to display multi-dimensional crowd analysis data.

[0092] The feedback module is used to feed back the medical information of emergency patients to the judgment module, and combine the character's motion data and human body attribute data to refine the character's physical condition and the cause of the character's injury or illness.

[0093] The specific implementation process is as follows: In actual medical situations, in addition to patients who are unable to move and are sent directly to the hospital for emergency treatment by ambulance, even patients with trauma or acute illnesses may go to the hospital for treatment on their own. During the way to the hospital, these patients' walking posture, walking speed, facial expressions, surface marks on their clothes and hand movements are different from those of ordinary people due to their injuries. In addition, these patients may ultimately find it difficult to reach the clinic on their own due to their injuries, and even if they arrive, the best time for treatment may be delayed.

[0094] Under this premise, the system collects images and tracks people across lenses, and then uses the behavior analysis module to analyze the patient's movements and personal attributes to determine whether the patient needs emergency treatment. Once it is determined that emergency treatment is needed, the reporting module will alert the emergency doctor and plan the fastest route for the emergency doctor. For example, a single patient with swollen feet due to gout is several times slower than an ordinary person in walking to the department in the hospital due to the pain, and accidentally falls on the way. At this time, through the collected patient images and the analysis of the patient's fall by the skeletal point analysis unit, the judgment module determines that the patient needs emergency treatment and preliminarily refines the injury into a lower limb disease. The reporting module then alerts the emergency doctor through the hospital terminal. The crowd analysis module analyzes the crowd density distribution in the spatial dimension, and the route planning unit plans a route that can reach the patient's location as quickly as possible. The emergency doctor leads the medical staff and carries the corresponding medical supplies to quickly reach the patient's location for emergency treatment and sends patients with difficulty in moving to the emergency room, thereby increasing the efficiency of emergency treatment for patients.

[0095] It is also possible that a victim with abdominal trauma needs to go to emergency treatment, but because the wound is difficult to stop bleeding and is in great pain, the human body surface color block analysis in the character attribute analysis unit can analyze that the victim has suffered trauma based on the blood on the victim's body surface, and the facial emotion analysis can show that the victim is in great pain. The judgment module calculates and determines that the victim needs emergency treatment based on the victim's speed and the spread of blood. The reporting module then alerts the emergency doctor, who quickly reaches the victim's location according to the fastest arrival route planned by the route planning unit, and initiates emergency treatment to send the victim to the emergency room to save the victim's life.

[0096] In addition, patients receiving hospital care may walk in open areas of the hospital and may fall or have other accidents. Since some places may be secluded and deserted corners, by the time others discover the patient, the best time for treatment may have been lost. Therefore, cameras covering the entire hospital can avoid such blind spots for rescue. When a patient falls, the image acquisition module collects images, the behavior analysis module analyzes the image, and the judgment module determines that the fallen patient needs emergency treatment. The reporting module alerts the emergency doctor and the attending physician of the hospitalized patient, and the route planning unit plans the fastest arrival route. The emergency doctor and the attending physician can quickly reach the patient's location to provide first aid and send the patient back to the ward.

[0097] After first aid and diagnosis, the patient's actual injury information is fed back to the behavior analysis module and the judgment module through the feedback module. The accuracy of the character attribute analysis and character situation judgment is calibrated through the deep learning algorithm, and the patient's injury data is refined to provide data support for the next patient's injury judgment, improve the accuracy of the system, and optimize the allocation of hospital emergency resources.

[0098] Example 2

[0099] The difference from the above embodiment is that, as shown in the attached Figure 5 As shown: the reporting module also includes a push unit, which is used to push real-time crowd flow changes on the routes to various departments to hospital visitors based on the unobstructed movement routes and crowd flow change predictions within the hospital.

[0100] Terminal applications also include personal terminals, which are used to push smooth routes or fastest arrival routes.

[0101] The specific implementation process is as follows: Due to the complex staff in the hospital, congestion is likely to occur in the building. The crowd analysis unit can analyze the crowd density distribution and attributes based on the crowd image information collected by the image acquisition module. Combined with the preset hospital three-dimensional map model, it can display the crowd distribution pattern for the hospital and optimize resource allocation. At the same time, it can also predict the trend of crowd changes through trajectory tracking, providing data support for the hospital's response.

[0102] After analyzing the distribution of crowd density, the route planning unit can plan a route with less crowd distribution. On the one hand, it can plan the fastest arrival route for emergency doctors, improve emergency efficiency, and grasp the first aid timing for patients. On the other hand, ordinary patients or visitors can use personal terminals, such as mobile apps and WeChat applets, to input the starting point and the department they want to reach. The route planning unit can plan a route that is not congested and display the changes in crowd flow on the route in real time, thereby realizing automatic diversion of medical personnel, improving medical efficiency and experience, and reducing the possibility of accidents in hospitals due to large crowds.

[0103] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0104] The above is only an embodiment of the present invention. Common knowledge such as the known specific structures and characteristics in the scheme is not described in detail here. Ordinary technicians in the field are aware of all common technical knowledge in the technical field of the invention before the application date or priority date, can obtain all existing technologies in the field, and have the ability to apply conventional experimental means before that date. Ordinary technicians in the field can improve and implement this scheme in combination with their own abilities under the inspiration given by this application. Some typical known structures or known methods should not become obstacles for ordinary technicians in the field to implement this application. It should be pointed out that for those skilled in the art, without departing from the structure of the present invention, several variations and improvements can be made, which should also be regarded as the scope of protection of the present invention. These will not affect the effect of the implementation of the present invention and the practicality of the patent. The scope of protection required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

Claims

1. A pedestrian tracking system based on behavior analysis, characterized by: It includes image acquisition module, cross-lens tracking module, behavior analysis module, judgment module, reporting module and feedback module; Image acquisition module, used to collect image information and environmental information of people within the hospital campus; Cross-camera tracking module, which is used to achieve seamless recognition and tracking of people between different cameras based on cross-camera tracking technology; The behavior analysis module is used to analyze the crowd image information and environmental information collected by the image acquisition module to obtain character movement data, human attribute data and crowd tracking data; The judgment module is used to judge the physical condition of the person and predict the cause of the person's injury or illness based on the person's motion data and body attribute data. It also plans smooth movement routes within the hospital and predicts changes in the flow of people based on the crowd tracking data. The reporting module is used to alert the hospital emergency room based on the person's physical condition and possible injuries. It also provides emergency doctors with the fastest arrival route based on the hospital's clear routes for priority rescue. It also notifies hospital visitors of changes in the flow of people to various departments based on the current clear routes and predicted changes in the flow of people within the hospital. The feedback module is used to feed back the medical information of emergency patients to the judgment module, and combine the character's motion data and human body attribute data to refine the character's physical condition and the cause of the character's injury or illness.

2. The pedestrian tracking system based on behavior analysis according to claim 1, characterized in that: The image acquisition module includes several surveillance cameras and several environmental sensors; Several surveillance cameras, including several indoor cameras installed in the hospital's outpatient hall and several outdoor cameras installed in the hospital campus, are used to collect image information of people throughout the hospital and in secluded corners; Several environmental sensors, including infrared cameras, temperature, humidity, and light sensors, are installed along with surveillance cameras to collect environmental information at different locations within the hospital.

3. The pedestrian tracking system based on behavior analysis according to claim 1, characterized in that: The cross-shot tracking module includes an image pre-processing unit, a person recognition unit, and a cross-shot matching unit; The image preprocessing unit preprocesses crowd image information, including denoising, enhancement, and correction, and uses image registration technology to spatially align images collected by different cameras; The person recognition unit is used to extract and identify the features of people in the crowd image information to establish a unique identification of the target; The cross-shot matching unit is used to establish associations between different cameras based on the feature identifiers of the characters to achieve cross-shot matching of the target.

4. The pedestrian tracking system based on behavior analysis according to claim 1, characterized in that: The behavior analysis module includes a skeleton point analysis unit, a character attribute analysis unit, and a crowd flow analysis unit; Skeleton point analysis unit, used to detect the temporal change motion data of a single human body and a single human body key point based on skeleton key point analysis technology; A person attribute analysis unit, used to detect person body surface attribute data and identify person action data based on person ID classification detection technology; The crowd analysis unit is used to count and track people based on deduplication crowd statistics technology.

5. The pedestrian tracking system based on behavior analysis according to claim 1, characterized in that: The judgment module includes a character situation judgment unit and a route planning unit; A character condition judgment unit is used to judge whether the character needs first aid and possible symptoms of injury or illness based on the character's motion data and body attribute data; The route planning unit is used to plan smooth movement routes within the hospital based on the crowd tracking data and the preset hospital route map.

6. The pedestrian tracking system based on behavior analysis according to claim 1, characterized in that: The reporting module includes an alarm unit, a push unit, and a terminal application; The alarm unit is used to alert the emergency doctor based on the first aid judgment result and possible injury symptoms of the person, and to plan the fastest arrival route for the emergency doctor based on the unobstructed movement routes within the hospital and provide prompts; The push unit is used to push real-time traffic flow changes on the routes to various departments to hospital visitors based on the unobstructed movement routes and traffic flow change predictions within the hospital; Terminal applications are used to prompt and promote smooth routes and real-time changes in pedestrian flow along these routes, and to display spatial pedestrian density distribution and temporal trends in pedestrian flow.

7. The pedestrian tracking system based on behavior analysis according to claim 4, characterized in that: The character attribute analysis unit includes human surface color block analysis, facial emotion analysis and character action recognition. Human body surface color analysis, used to analyze clothing color, stains and bloodstains on the human body surface; Facial emotion analysis, used to analyze emotional tendencies based on facial expressions; Human action recognition, used to identify human actions.

8. The pedestrian tracking system based on behavior analysis according to claim 4, characterized in that: The crowd flow analysis unit includes multi-dimensional crowd flow analysis, crowd flow attribute analysis and crowd flow trajectory tracking. Multi-dimensional crowd flow analysis is used to analyze the spatial dimension of crowd density distribution, record historical crowd flow data, and calculate the trend of crowd flow changes in the time dimension based on the preset three-dimensional map of the hospital; Crowd attribute analysis, used to combine with the person attribute analysis unit to analyze the gender, age, and facial expression of the crowd, providing hospitals with refined crowd portraits; Crowd flow tracking is used to track the flow of people and predict the real-time trend of crowd density changes.

9. The pedestrian tracking system based on behavior analysis according to claim 5, characterized in that: The character situation judgment unit includes first aid judgment and injury details. First aid judgment, which uses deep learning algorithms to train models to determine whether a person has symptoms of injury or illness and whether first aid is needed; Injury refinement is used to determine the possible types of injuries a character may have based on their motion data and body attribute data.

10. The pedestrian tracking system based on behavior analysis according to claim 6, characterized in that: Terminal applications include personal terminals and hospital terminals. Personal terminals are used to push smooth routes or the fastest arrival routes; Hospital terminal, used to display multi-dimensional crowd flow analysis data.

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