Method for arranging plurality of sensors and interactive sports system using same

Abstract

The present invention relates to an interactive sports system using an arrangement of a plurality of sensors, including a control unit for controlling a method of arranging a sensor, such as a LiDAR, capable of effectively detecting two or more multi-objects in a situation where mutual occlusion and interference by an object occur, wherein the interactive sports system includes: a sensor capable of detecting a multi-object through a LiDAR, a camera, and a radar; a sensor control unit that optimizes a sensor arrangement on the basis of the position and state of an object detected by the sensor; a control unit that collects data from the sensor and determines the position and state of the object by analyzing the collected data; a main arm formed to extend to the periphery of the control unit so as to arrange the sensor at various angles around the object; and an auxiliary arm attached to the main arm to be rotated at a predetermined angle, thereby adjusting the position of the sensor by finely rotating the auxiliary arm to complement a viewing angle between the sensors.

Description

Method for arranging multiple sensors and interactive sports system using the same

[0001] The present invention relates to an interactive sports system using a plurality of sensor arrangements, and more particularly, to an interactive sports system using a plurality of sensor arrangements, including a control unit that controls a sensor arrangement method such as Lidar that can effectively detect two or more multiple objects in a situation where mutual occlusion and interference by objects occur.

[0002] Early sensor technologies focused on detecting single objects or limited scenarios, but in multi-object detection, mutual occlusion or interference between objects caused problems in detection efficiency and accuracy;

[0003] Existing sensor systems often operate in fixed locations or angles, making it difficult to respond flexibly to various environments or conditions.

[0004] In complex environments or with irregular object placement, existing sensors often fail to perform detection effectively, especially in dynamic sports or entertainment environments.

[0005] Blind spots in detection occur due to the fixed angle or position of the sensor, which limits overall situational awareness.

[0006] Existing sensor deployment and control technologies lack flexibility and have structures that make it difficult to actively respond to real-time environmental changes, which limits the accuracy and efficiency of multi-object detection.

[0007] The lack of real-time data processing and analysis capabilities has reduced the response speed and accuracy of sensors, limiting their use in dynamic and complex environments.

[0008] Nevertheless, the need for convergence of various sensor technologies such as LiDAR, cameras, and radar has emerged, and the development of technologies that combine the strengths of each sensor to enable effective detection even in complex environments has been demanded;

[0009] There was a need to improve detection range and accuracy, and increase adaptability to various environments, through optimization of sensor placement and real-time adjustment capabilities.

[0010] A comprehensive system design that integrates sensor placement and control was required, enabling stable and accurate multi-object detection in a variety of environments.

[0011] The development of advanced algorithms and software for real-time analysis and processing of sensor data was also essential.

[0012] Against this backdrop, the present invention aims to improve a sensor placement method and a control unit to overcome existing limitations and enhance the efficiency and accuracy of multi-object detection, and in particular, to increase applicability in dynamic environments such as sports and entertainment.

[0013] To achieve this, the convergence of various sensor technologies, real-time data processing and analysis, and the development of flexible sensor deployment and control technologies are essential.

[0014] The present invention has been devised to improve the aforementioned problems, and the purpose of the present invention is to provide a sensor arrangement method capable of effectively detecting multiple objects even in a situation where mutual occlusion and interference occur due to objects, and an interactive sports system utilizing a plurality of sensor arrangements using the method.

[0015] In addition, according to an embodiment of the present invention, the purpose is to provide an interactive sports system using a plurality of sensor arrangements that maximizes the accuracy and efficiency of multi-object detection by optimizing the position and angle of the sensors, and allows the entire system to detect and analyze the surrounding environment more accurately and effectively by adjusting the position and angle of each sensor.

[0016] In order to achieve the above object, one embodiment of the present invention includes: a sensor capable of detecting multiple objects through LiDAR, a camera, and radar; a sensor control unit that optimizes the arrangement of the sensors based on the positions and states of the objects detected by the sensors; a control unit that collects data from the sensors and analyzes the collected data to determine the positions and states of the objects; a main arm formed by extending around the control unit so as to place the sensors at various angles around the object; and an auxiliary arm attached to the main arm and rotated at a certain angle; wherein the positions of the sensors are adjusted by finely rotating the auxiliary arm so that the viewing angles between the sensors are complementary to each other.

[0017] The control unit transmits the optimized sensor placement results to the sensor control unit and controls the sensor control unit to adjust the position according to the transmitted results.

[0018] The above control unit adjusts the placement position and angle of the sensors to minimize occlusion or interference by multiple objects through the sensor control unit when occlusion or interference occurs due to two or more objects, and to cover up the insufficient detection of objects passing through multiple areas.

[0019] A method for detecting an object using a LiDAR sensor is provided. The method comprises: a LiDAR sensor that measures the distance, direction, speed, etc. of an object by emitting a laser; a control unit that controls the arrangement position and angle of the LiDAR sensor; a method using an interactive sports system using a plurality of sensor arrangements, the method comprising: a step in which the control unit receives data measured from the LiDAR sensor; a step in which the control unit analyzes the received data to track the position and movement of an object; and a step in which the control unit adjusts the arrangement position and angle of the LiDAR sensor according to the position and movement of the object.

[0020] The method further includes a step of finely adjusting the placement position and angle of the LiDAR sensor to minimize occlusion and interference by two or more surrounding objects and to cover undetected parts in an area when an object passes through multiple areas.

[0021] The method further includes a step of detecting and tracking an object by generating a high-resolution three-dimensional image of the surrounding environment through a process in which the LiDAR sensor fires a laser to measure the distance, direction, and speed of the object.

[0022] The control unit further includes a step of receiving data measured from the sensor, analyzing the data to track the position and movement of an object, and then using the information to determine and adjust the optimal position and angle of the LiDAR sensor in order to control the placement position and angle of the LiDAR sensor.

[0023] According to one embodiment of the present invention, a sensor arrangement method capable of effectively detecting multiple objects in a situation where mutual occlusion and interference phenomena occur due to objects can be provided.

[0024] In addition, according to one embodiment of the present invention, a system can be provided that precisely recognizes the location and coordinate values ​​of multiple objects and automatically calibrates sensors in real time to improve detection accuracy.

[0025] In addition, in order to effectively detect multiple objects, the length of the device can be variably adjusted, and a sensor placement method that can be adjusted to any angle on a plane can be provided, and an interactive sports system and device using the same can be provided.

[0026] Furthermore, the present invention enables precise object detection and tracking even in complex environments, and contributes to enhancing the functionality and efficiency of interactive sports systems and devices.

[0027] In addition, the present invention provides an interactive sports system that accurately detects the location and status of an object in various sports environments and minimizes blind spots by optimizing sensor placement.

[0028] In particular, the present invention is effective for indoor sports with a large number of participants, such as boccia or running, and can enhance the interactive experience of indoor sports games by analyzing data collected from multiple sensors to identify the location and status of objects.

[0029] In addition, the present invention eliminates blind spots that are difficult to recognize with sensors in fixed positions, and overcomes limitations due to the straightness of lidar sensors, thereby enabling more accurate and effective object detection.

[0030] FIG. 1 is a diagram schematically showing the relationship between components that implement sensor placement optimization for multi-object detection according to one embodiment of the present invention.

[0031] FIGS. 2 and 3 are drawings schematically showing the relationship between components that implement an interactive sports system using a plurality of sensor arrangements according to one embodiment of the present invention.

[0032] FIG. 4 is a flowchart schematically illustrating an object detection method using a LiDAR sensor according to one embodiment of the present invention.

[0033] FIG. 5 is a drawing showing a process for determining the optimal placement position and angle of a LiDAR sensor according to one embodiment of the present invention.

[0034] FIG. 6 is a drawing showing a process for determining the optimal placement position and angle of a LiDAR sensor according to another embodiment of the present invention.

[0035] Figure 7 is a Python code showing camera data collection (using OpenCV).

[0036] Figure 8 is a Python code showing LiDAR data collection (an example using the PCL library).

[0037] Figure 9 is a Python code showing radar data collection.

[0038] Figure 10 is a Python code that preprocesses each piece of data detected by the sensor.

[0039] Figure 11 is a diagram showing the use of an indoor boccia sport in which multiple participants throw a ball into a court indoors and a sensor recognizes the position of the ball.

[0040] Figure 12 is a drawing showing how it is used in an indoor sport such as running, where three or more participants appear.

[0041] It should be noted that this is not the case. In addition, the technical terms used in the present invention should be interpreted as having a meaning generally understood by a person of ordinary skill in the technical field to which the present invention belongs, unless specifically defined to have a different meaning in the present invention, and should not be interpreted in an excessively comprehensive or excessively narrow sense. In addition, when the technical terms used in the present invention are incorrect technical terms that do not accurately express the spirit of the present invention, they should be replaced with technical terms that can be correctly understood by a person of ordinary skill in the art. In addition, the general terms used in the present invention should be interpreted as defined in the dictionary or according to the context, and should not be interpreted in an excessively narrow sense.

[0042] Additionally, singular expressions used in the present invention include plural expressions unless the context clearly dictates otherwise. In the present invention, terms such as "consist of" or "include" should not be construed to necessarily include all of the components or steps described in the invention, and should be construed to mean that some of the components or steps may not be included, or that additional components or steps may be included.

[0043] Hereinafter, a preferred embodiment of the present invention will be described in detail with reference to the attached drawings. Regardless of the drawing numbers, identical or similar components are given the same reference numbers, and redundant descriptions thereof will be omitted.

[0044] Furthermore, when describing the present invention, detailed descriptions of related known technologies will be omitted if they are deemed to obscure the gist of the present invention. Furthermore, it should be noted that the attached drawings are intended solely to facilitate understanding of the spirit of the present invention and should not be construed as limiting the spirit of the present invention.

[0045] As illustrated in FIGS. 1 to 4, the present invention relates to a sensor placement optimization device for multiple object detection, which includes two main components.

[0046] The sensor (110) is a sensor capable of detecting multiple objects, such as LiDAR, camera, and radar. The sensor (110) detects the surrounding environment and collects data.

[0047] Various types of sensors (110) have the ability to detect multiple objects, such as LiDAR, camera, and radar.

[0048] Sensors detect various objects in the surrounding environment and collect this information.

[0049] The control unit (120) collects data from the sensor (110) and analyzes the collected data to determine the location and status of the object. The control unit (120) optimizes the sensor placement based on the location and status of the object.

[0050] The control unit (120) analyzes data collected from the sensors. Through analysis, the control unit determines the location and status of the object and optimizes the sensor placement based on this information. The optimized sensor placement results are transmitted to the sensors, and the sensors adjust their positions based on the results received from the control unit.

[0051] The optimized sensor placement results are transmitted from the control unit (120) to the sensor (110). The sensor (110) adjusts its position according to the results transmitted from the control unit (120).

[0052] Therefore, the efficiency of multi-object detection is enhanced by continuously optimizing sensor placement through sensor data collection and the control unit's analysis capabilities. This allows for real-time response to changing environments, maximizing the accuracy and efficiency of object detection.

[0053] Specifically, as shown in FIG. 1, the present invention allows two or more LiDAR sensors to be freely positioned within areas A and B, thereby precisely recognizing and detecting the positions and coordinate values ​​of multiple objects. The present invention includes a function capable of automatically calibrating the positioned LiDAR sensors in real time. This enables more accurate and efficient object detection and location tracking.

[0054] For example, the control unit (120) collects data from the sensor (110) and analyzes it to accurately determine the location and status of an object. The control unit is connected to a main arm (115) to position the sensor at various angles around the object. The main arm (115) extends around the control unit and serves to adjust the direction of the sensor.

[0055] The auxiliary arm (116) is attached to the main arm (115) and can rotate at a certain angle. This auxiliary arm allows for more precise adjustment of the sensor's position. This precise adjustment allows the field of view between the sensors (110) to complement each other, thereby enhancing overall detection capability.

[0056] Therefore, the present invention optimizes the position and angle of sensors to maximize the accuracy and efficiency of multi-object detection. By adjusting the position and angle of each sensor, the entire system can more accurately and effectively detect and analyze the surrounding environment. This optimization is especially important in dynamic environments, and the present invention enables more effective object detection and analysis in such environments.

[0057] As a first embodiment, if there is overlap between sensors, an object may be detected more than once by a single sensor. This can make it difficult to accurately determine the object's location and status. Therefore, minimizing sensor overlap is crucial when deploying sensors.

[0058] As a second embodiment, the following method is used to optimize the field of view between sensors in the present invention.

[0059] (1) Calculate the optimal viewing angle based on the size and location of the object.

[0060] (2) Position the sensors so that their field of view overlaps.

[0061] (3) The sensors are arranged so that their field of view is complementary to each other.

[0062] As a third embodiment, a method for calculating an optimal viewing angle according to the size and position of an object in the present invention is as follows.

[0063] (1) Measure the size of the object.

[0064] (2) Set the location where the object should be detected.

[0065] (3) The sensor calculates the range within which the object can be seen based on the object's size and location.

[0066] As a fourth embodiment, the method of arranging the sensors so that their viewing angles overlap in the present invention is as follows.

[0067] (1) Multiple sensors are arranged symmetrically or similarly around the object.

[0068] (2) Adjust the position of the sensors so that the field of view between the sensors overlaps.

[0069] Therefore, it is also possible to detect objects in three dimensions by detecting objects with overlapping field of view.

[0070] As a fifth embodiment, the method of arranging the sensors so that their viewing angles are complementary to each other in the present invention is as follows.

[0071] (1) The main arm (115) is extended to the periphery so that the sensors can be placed at various angles around the object, and then the auxiliary arm (main arm; 116) is rotated at a certain angle.

[0072] (2) The position of the sensor is adjusted by slightly rotating the auxiliary arm (116) so that the viewing angles between the sensors complement each other.

[0073] Accordingly, in order to effectively detect two or more objects as shown in FIG. 2 and the drawing, it is possible to provide a method for arranging sensors of a lidar that can be adjusted to any angle on a plane as shown in FIG. 4, as well as an interactive sports system and device using the same, in addition to variably adjusting the length of a device such as the left and right arms (115) on which sensors such as lidar are arranged.

[0074] As illustrated in Figure 5, the sensor placement and arm adjustment are optimized for the user's environment, providing more precise and reliable data. This enhances the user experience of interactive sports systems and enables new types of sports and recreational activities.

[0075] As an example, the present invention comprises a data receiving step (S101); a data analysis and tracking step (S102); a LiDAR sensor adjustment step (S103); and the like.

[0076] In the data reception step (S101), the control unit receives data measured from the LiDAR sensor. This data includes essential information such as the distance, direction, and speed of the object.

[0077] In the data analysis and tracking step (S102), the received data is analyzed by the control unit, allowing the object's location and movement to be tracked. Accurate data analysis is essential during this process to accurately determine the object's location and movement.

[0078] In the LiDAR sensor adjustment step (S103), the control unit adjusts the LiDAR sensor placement and angle based on the object's position and movement. This step is essential for maximizing sensor efficiency and improving object detection accuracy.

[0079] Therefore, the present invention can effectively detect multiple objects by minimizing occlusion and interference caused by two or more objects.

[0080] In particular, the placement and angle of the LiDAR sensor can be adjusted to prevent missed detection in each area when an object passes through multiple areas. This significantly improves the accuracy and reliability of object detection.

[0081] As illustrated in FIG. 6, the sensor module (110) includes one or more sensors for collecting data such as position, size, speed, and direction, and the data collected from the sensors is transmitted to the control unit.

[0082] The control unit (120) analyzes data received from the sensor module (110) to determine the location and status of the object, and optimizes the sensor placement to minimize interference between sensors.

[0083] The sensor control unit (150) adjusts the position of the sensor according to the sensor placement results transmitted from the control unit (120).

[0084] That is, the sensor module (110) collects necessary data, and this data is analyzed in the control unit (120) to lead to optimization of sensor placement.

[0085] Based on the analysis results of the control unit (120), the sensor control unit (150) adjusts the positions of the sensors to minimize interference between sensors and optimize the performance of the entire system.

[0086] As an example, the present invention may include the following steps for developing Python code necessary to implement a sensor placement optimization device for multiple object detection.

[0087] (1) During the sensor data collection phase, various data are collected from LiDAR, cameras, and radar sensors. For this purpose, the API or SDK for each sensor can be used. For example, OpenCV can be used for cameras, and PCL (Point Cloud Library) can be used for LiDAR.

[0088] (2) During the data preprocessing and integration stage, the collected data must be appropriately preprocessed and data from different sensors must be integrated. For example, this may involve aligning point clouds from LiDAR data with camera images.

[0089] (3) In the object detection and location identification step, objects are detected and located based on the integrated data. For this purpose, machine learning or computer vision algorithms can be used. For example, deep learning-based object detection models such as YOLO and SSD can be utilized.

[0090] (4) In the sensor placement optimization stage, the sensor placement is optimized by analyzing the object's location and status. This can be implemented using mathematical optimization algorithms (e.g., genetic algorithms, simulated annealing, etc.).

[0091] (5) Develop a control algorithm that transmits the optimized sensor placement results from the control unit implementation stage to the sensor control unit, and allows the sensor control unit to adjust the sensor positions based on these results. This may require integration with specific hardware interfaces.

[0092] (6) The web application integration step visualizes the collected and processed data and optimization results through a web application. This can be implemented using a Python web framework like Flask or Django.

[0093] As illustrated in FIG. 7, for the sensor data collection step, example code for collecting LiDAR, camera, and radar sensor data is provided.

[0094] The Python code for each drawing provides a basic framework, and in a real environment, adjustments may be necessary depending on the sensor's API or SDK.

[0095] Specifically, to collect camera data, we first attempt to connect to the camera device. Here, we use the `cv2.VideoCapture` function to connect to the camera through the specified camera index. If the camera connection fails during this process (i.e., `cap.isOpened()` returns `False`), a message indicating that the camera is not properly connected is displayed and no further processing is performed. This is a crucial step in verifying the accessibility of the camera device.

[0096] Next, if the camera device is connected properly, try capturing a frame.

[0097] At this point, the `cap.read()` function is called to read a frame from the camera. This function returns `ret`, which indicates success or failure, and `frame`, which is the captured frame itself. If `ret` is `False`, a message indicating that frame capture failed is printed and the process is terminated.

[0098] Finally, after successfully capturing a frame, release the resources used. Call the `cap.release()` function to safely terminate the connection with the camera device. This step is crucial for resource management.

[0099] This entire process provides the basic flow for collecting camera data, but is not limited to it.

[0100] As shown in Fig. 8, LiDAR data collection must be implemented according to the specific SDK of the LiDAR being used, and this is an example code using the PCL library to implement it.

[0101] Specifically, this process aims to collect 3D spatial data from LiDAR sensors. LiDAR sensors emit light and measure its reflections, creating a 3D map of the surrounding environment. This data is typically represented in a form called a point cloud, where each point contains the coordinates of a single point in space and additional information (e.g., the intensity of the reflection).

[0102] The first step is to connect and initialize the LiDAR sensor. This depends on the LiDAR's specific Software Development Kit (SDK). Depending on the LiDAR device in use, a different SDK is required, providing detailed device functionality and data access methods. The PCL library works with these SDKs to provide the tools and algorithms necessary to process point cloud data.

[0103] The data collection process requires real-time data acquisition from the LiDAR device (3). This can be continuous or triggered by specific events. The collected data is stored in the form of a point cloud, providing a precise 3D representation of the physical space.

[0104] Finally, the collected data is passed on to the next step for analysis, processing, or storage. For example, this data can be utilized for various purposes, such as environmental modeling, distance measurement, and object detection. The PCL library provides various tools and functions for processing this point cloud data, facilitating data analysis and processing.

[0105] As shown in Figure 9, radar data collection is also implemented differently depending on the SDK or API of the radar being used.

[0106] Specifically, the radar data collection claims encompass a process implemented using a radar system's Software Development Kit (SDK) or Application Programming Interface (API). A radar system is a technology that uses electromagnetic waves to detect the position, velocity, and other characteristics of an object.

[0107] The first step is to establish communication with the radar system. This involves configuring an interface with the radar system using the radar device's SDK or API. The SDK or API provides programmatic access to the radar system's functions and provides instructions on how to collect and process data.

[0108] During the data collection process, data captured by the radar system is retrieved in real time. Radar data primarily takes the form of reflected electromagnetic waves and contains information such as distance, speed, and angle to an object. This data provides valuable information about the surrounding environment and can be used for object detection, speed measurement, and environmental monitoring.

[0109] Finally, the collected radar data is stored or transmitted for further processing. This data can be utilized for various purposes, including analysis, visualization, and documentation. Depending on the purpose of each application, the data can be processed as needed or analyzed using specific algorithms.

[0110] The above code provides a basic framework, and in a real environment, many parts may differ depending on the type, model, and connection method of the sensor.

[0111] In addition, the present invention comprises several steps: preprocessing and integration of sensor data, object detection and location identification, sensor placement optimization, control unit implementation, and web application integration. A detailed description of each step will be provided.

[0112] As illustrated in Figure 10, data collected from various sensors, such as cameras, LiDAR, and radar, is processed and integrated. Camera data undergoes image processing techniques using OpenCV, LiDAR data undergoes point cloud noise removal, and radar data undergoes preprocessing, including signal filtering and conversion. After this processing, data from each sensor is integrated and used as needed.

[0113] Next, we use the processed sensor data to identify objects and determine their locations. For this purpose, a deep learning-based object detection model like YOLO can be used. For example, this could include using a YOLO model to detect objects in camera images. This step is implemented using a deep learning framework like Python's TensorFlow or PyTorch.

[0114] Additionally, in the sensor placement optimization step, an algorithm is implemented to optimize the placement of sensors based on the location and status of the object.

[0115] For example, genetic algorithms can be used to adjust the position and orientation of sensors to achieve optimal coverage. This involves creating a population with genes that indicate the position and orientation of sensors, and then adjusting the sensor placement to maximize coverage of the targets they are intended to detect.

[0116] The control unit implementation step according to the present invention is a step of transmitting the optimized sensor placement results to the sensor control unit and implementing logic for adjusting the position of the sensor accordingly.

[0117] This process may involve interfacing with hardware interfaces, and the actual control unit implementation must take into account various factors such as the characteristics of the sensor, the connected hardware interface, and the network protocol.

[0118] The final step, web application integration, involves visualizing the collected and processed data and optimization results through a web application. A simple web application is built using a Python web framework like Flask, and the sensor data and optimized sensor placement results are displayed on a remotely monitorable web page.

[0119] Each step contributes to enhancing the functionality and efficiency of the present invention, which can be applied to various interactive sports. Data preprocessing and integration are crucial for increasing the accuracy and reliability of sensor data, and object detection and localization processes can also occur within different programming environments.

[0120] Meanwhile, as an example of an interactive sports system using a plurality of sensor (110) arrangements, including a sensor (110) capable of detecting multiple objects through LiDAR, camera, and radar, a sensor control unit (150) that optimizes the sensor arrangement based on the position and state of the object detected by the sensor (110), and a control unit (120) that collects data from the sensor (110) and analyzes the collected data to determine the position and state of the object, in the image processing part, it is possible to implement the system using various computer vision libraries (halcon, evision, etc.) other than opencv and yolo, and various open source, paid / free libraries other than models such as yolo, but is not limited thereto.

[0121] As an example, the invention can also be used in boccia or running, which are similar sports.

[0122] As illustrated in FIG. 11, in the case of an indoor boccia sport in which a number of participants throw a ball into a stadium indoors and a sensor (110) recognizes the position of the ball, as an embodiment or application example of the present invention, if three or more balls exist in the stadium and a blind spot occurs that is difficult to recognize with a fixed-position lidar, the control unit (120) can eliminate the blind spot through the above-described open source or library, so that it can be utilized in indoor interactive sports.

[0123] Additionally, as illustrated in Fig. 12, it can be utilized in an indoor sport such as running, where three or more participants appear.

[0124] In addition, the control unit (120) can optimize the search for the object location by controlling the placement of three or more sensors (110) to facilitate the search for blind spots through a method of placing two lidar or other sensors (110) or a method of placing a lidar or other sensor in a fixed position, and to overcome the limitations of the search for the object location due to the straightness of the lidar sensor.

[0125] The present invention relates to an interactive sports system utilizing a plurality of sensor arrangements, including a control unit that controls a method of placing sensors such as Lidar that can effectively detect two or more multiple objects in a situation where mutual occlusion and interference by objects occur. The present invention provides an interactive sports system that accurately detects the location and status of objects in various sports environments and minimizes blind spots by optimizing sensor arrangement.

[0126] In particular, the present invention is effective for indoor sports with a large number of participants, such as boccia or running, and can enhance the interactive experience of indoor sports games by analyzing data collected from multiple sensors to identify the location and status of objects.

[0127] In addition, the present invention eliminates blind spots that are difficult to recognize with sensors in fixed positions, and overcomes limitations due to the straightness of lidar sensors, thereby enabling more accurate and effective object detection.

Claims

1. A sensor (110) capable of detecting multiple objects through LiDAR, camera, and radar; A sensor control unit (150) that optimizes the sensor placement based on the location and status of the object detected by the sensor (110); A control unit (120) that collects data from the above sensor (110) and analyzes the collected data to determine the location and status of the object; A main arm (115) formed to extend around the periphery of the control unit (120) so as to place the sensors at various angles around the object; Including an auxiliary arm (116) attached to the main arm (115) and rotated at a certain angle; An interactive sports system utilizing a plurality of sensor arrangements, characterized in that the positions of the sensors are adjusted by finely rotating the auxiliary arm (116) so that the viewing angles between the sensors (110) are complementary to each other.

2. In claim 1, An interactive sports system using a plurality of sensor arrangements, characterized in that the control unit (120) transmits the optimized sensor arrangement results to the sensor adjustment unit (150) and controls the sensor adjustment unit (150) to adjust the position according to the transmitted results.

3. In claim 1, The above control unit (120) is an interactive sports system using a plurality of sensor arrangements, characterized in that, when an obscuration or interference phenomenon occurs due to two or more objects, two or more sensors arranged at arbitrary locations in areas A and B adjust the arrangement positions and angles of the sensors to minimize obscuration or interference by multiple objects through a sensor adjustment unit and to cover areas where detection is insufficient for objects passing through multiple areas.

4. A method using an interactive sports system using a plurality of sensor arrangements including a sensor (110) that measures the distance, direction, speed, etc. of an object by emitting a certain wavelength; and a control unit (120) that controls the arrangement position and angle of the sensor; A step (S101) in which the above control unit receives data measured from the sensor; A step (S102) in which the control unit analyzes the received data to track the location and movement of the object; A method for arranging multiple sensors, characterized in that it includes a step (S103) in which the control unit adjusts the arrangement of the sensors according to the position and movement of the object.

5. In claim 4, A method for arranging multiple sensors, characterized by further comprising the step of finely adjusting the placement positions and angles of the sensors to minimize obscuration and interference by two or more surrounding objects and to cover undetected parts in areas when objects pass through multiple areas.

6. In claim 4, A method for arranging multiple sensors, characterized in that it further includes a step of generating a high-resolution three-dimensional image of the surrounding environment through a process in which the sensor (110) emits a certain wavelength to measure the distance, direction, and speed of the object, thereby detecting and tracking the object.

7. In claim 4, A method for arranging multiple sensors, characterized in that it further includes a step of receiving data measured from a sensor, analyzing the data to track the position and movement of an object, and then using the information to determine and adjust the optimal position and angle of the sensor, in order for the control unit (120) to control the position and angle of the sensor arrangement.

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