CN107976685B - Indoor human target tracking system of infrared sensor based on thing networking - Google Patents
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Abstract
The invention relates to an indoor human body target tracking system of an infrared sensor based on the Internet of things. The sensor platform of the Internet of things based on the Contiki system is built, the field test can be carried out through a serial port, the remote test can also be carried out through an Ethernet card, and the limitation that the traditional sensor network can only carry out the field test is solved; designing a side-looking and overlooking infrared sensor network omnibearing sensing model, and comprehensively using side-looking nodes and overlooking nodes of approximate concentric rings of a detection area in a double-layer design to obtain the position information of a human body target; the human body target is positioned by combining a sensor measuring line crossing method and detection area division, so that the system installation difficulty and the calculated amount are reduced; smoothing the track by adopting a Kalman filtering algorithm, and further matching the motion tracking track of the target with a target model to realize human target tracking; and deducing and predicting the behavior of the human body target according to the tracking information of the target, the motion model and the arrangement of the actual scene.
Description
Technical Field
The invention relates to a human body target tracking system, in particular to an infrared sensor indoor human body target tracking system based on the Internet of things.
Background
In recent years, human target tracking technology is continuously developed and successfully applied to the military and civil fields, and has wide application prospects in the aspects of early warning and security detection. At present, a video and image processing method is mostly used for tracking human body targets, tracking is realized by extracting image features of the targets, a high-definition camera, a larger storage space and a high-performance processor are needed for the tracking, a tracking system is expensive, requirements for environmental factors such as light and the like are high, and target perception and tracking based on visible light are difficult to realize when visibility is low. In addition, video-based human target tracking requires saving specific image features such as human faces and motions, and in some occasions, the problem of security and privacy exists. The pyroelectric infrared sensor is a passive infrared sensor which is low in cost and convenient for mass production, has no excessive requirements on environmental factors such as light rays and the like, does not involve privacy invasion, and is very suitable for tracking human targets. However, the sensor can only acquire target azimuth information and is lack of distance information, so that a distributed networked layout is necessary, a sensor detection model is skillfully designed, and the positioning and tracking of a human target are realized by utilizing multi-sensor cooperative sensing.
In the field of distributed data acquisition, wireless sensor networks with the characteristics of low power consumption, low cost and ad hoc networks are widely used. However, most of the existing wireless sensor networks adopt a Zigbee system, and only communication between sensors can be completed, and direct communication with the internet cannot be achieved, so that data acquisition can be performed only on site. The Contiki system is an operating system specially designed for the Internet of things and a wireless sensor network, and comprises a uIP protocol stack, can be seamlessly connected with the Internet, and can be used for acquiring field data and remote data through the Internet. At present, wireless sensor network nodes for indoor human body target tracking all adopt an overlook sensing mode or a side-looking sensing mode of combining an infrared sensor with a Fresnel lens. In the existing side-looking perception system, the human body target tracking precision is low due to the fact that the node size is small and the view field is too large, or the nodes are specially made for thinning the view field and are inconvenient to install due to the fact that the node size is too large. In addition, with single side-looking perception, human target occlusion problems may occur. And a single overlook perception is adopted, a plurality of nodes are required to be installed to refine the detection area in order to improve the positioning accuracy, the number of the nodes is large, and the installation difficulty and the calculation amount of processed data are increased. Therefore, a brand new design needs to be performed on the current infrared sensing mode, and balance and optimization are performed among the system size, the installation difficulty, the positioning accuracy and the calculated amount.
The invention provides an indoor human body target tracking system of an infrared sensor network combining side view and overlook according to the characteristics and the limitations of two sensing modes of side view and overlook. The infrared sensor is equipped with an optical reflection device, and the size is reduced and the purpose of further thinning the view field is achieved through reasonable arrangement of the sensor. In order to solve the problem of human body target shielding, a small number of overlooking infrared sensor nodes are used in an auxiliary mode, the overlooking sensing view field area division of the sensor is combined with a side-looking sensing line crossing method, and the calculated amount is reduced while more positioning information is obtained.
Disclosure of Invention
The invention provides an infrared sensor indoor human body target tracking system based on the Internet of things, which comprises the steps of building a sensor platform of the Internet of things, designing a side-view and overlook infrared sensor network omnibearing sensing model, positioning and tracking a human body target and predicting human body target behaviors by combining with an actual scene.
The establishment of the sensor platform of the internet of things based on the Contiki system is the basis of data acquisition and data transmission, the platform comprises a plurality of information acquisition nodes and a sink node, the information acquisition nodes send acquired information to the sink node, the sink node is connected with the Internet directly or through a serial port to transmit the received information to an upper computer, and tracking and behavior prediction of a field or remote human body target are realized through further information processing.
Furthermore, the information acquisition node comprises an infrared sensor module, a single chip microcomputer module, a radio frequency module and a power supply module, and the sink node comprises a single chip microcomputer module, a radio frequency module, a serial port module, an Ethernet card module and a power supply module.
Furthermore, the infrared sensor module is used for collecting human body target information, and the output voltage of the sensor changes along with the change of the motion state of the human body target; the single chip module is provided with a Contiki system, supports ad hoc networks among nodes and direct connection with the Internet, and is used for carrying out primary processing on data acquired by the infrared sensor, including carrying out binarization processing on output signals of the sensor, wherein a human body target is sensed by '1' and a human body target is not sensed by '0'; the radio frequency module is used for realizing communication between the information acquisition node and the sink node, and comprises data transmission of the information acquisition node and data reception of the sink node; the serial port module and the Ethernet card module respectively realize that the information of the sink node is transmitted to an upper computer in a serial port or Ethernet mode so as to meet the requirements of field data or remote data acquisition; the power module uses a dry battery to supply power to the node.
Infrared sensor looks sideways at-overlooks all-round target perception model and includes and looks sideways at node and overlook node two parts, in the indoor space, node position is fixed, and the space arranges a plurality of side view nodes all around, can evenly install on the wall according to actual environment, and the space middle part uses overlooks the node and fixes on the ceiling or 3m eminence, looks sideways at the node and overlooks the node adjustable in number to all positions that detection area can cover the target and probably appear are accurate. And performing cooperative processing on the information acquired by the side view node and the overlook node to obtain the position information of the human body target.
Furthermore, the side-looking node is designed in a double-layer mode, the pyroelectric infrared sensors are provided with infrared optical reflection devices to realize side sensing, the side-looking node is installed on a wall, the height from the ground is less than 1m, the side-looking node is designed into an upper layer and a lower layer within a sensing range of 180 degrees, 4 infrared sensors are arranged on each layer, the sensing angle of each sensor is 90 degrees, and the sensing range is refined to be 22.5 degrees after the sensing ranges of different sensors are overlapped; when the side view node is arranged at a corner position, a total sensing range of 90 degrees is needed, at least 2 sensors on each layer are needed at the position, the total number of the sensors is 4, and in order to reserve margin, the invention uses 3 sensors on each layer, and the total number of the sensors is 6; the overlooking nodes are hung on a ceiling or at a height of 3m from the ground, each node uses 4 infrared sensors, each sensor is provided with a Fresnel lens to realize overlooking perception, and the ground area is divided into 4 approximately concentric circles for positioning and tracking a human target.
Further, the specific steps of human target tracking are as follows:
(1) according to the output of the side-looking sensor nodes, activating corresponding measuring lines, taking the positions of the sensor nodes as a reference, if the end points corresponding to the refined sensing regions exactly contain the reference positions, activating longer angle bisectors of the refined sensing regions as measuring lines, otherwise, making diagonal lines of the refined sensing regions exactly passing through the reference positions as measuring lines, regarding cross points generated among the measuring lines as primary measuring points, regarding a human body target, only part of the cross points can reflect the real position of the target, and the rest of the cross points are false measuring points to be removed;
(2) according to the output of the overlooking sensor node, activating a region with a target, calling the region as a primary sensing domain, eliminating primary measuring points which do not exist in the primary sensing domain in the step (1), and reserving the rest measuring points to be used as secondary measuring points;
(3) if one secondary measuring point is in the thinning sensing range of the activated measuring line of a certain side-looking node, or the secondary measuring point exceeds the total sensing range of the side-looking node, the reliability of the secondary measuring point is increased; for each secondary measuring point, traversing all side-looking nodes to respectively obtain the reliability of the side-looking nodes;
(4) reserving the secondary measuring point with the highest intensity in the step (3) as an effective measuring point, and matching the effective measuring point with the corresponding target by using a multi-dimensional distribution method to further obtain the positioning point of each target;
(5) and smoothing the track by adopting a Kalman filtering algorithm, and further matching the motion tracking track of the target with a target model to realize target tracking.
Furthermore, the behavior of the human body target can be predicted according to the tracking information of the human body target, the corresponding model and the arrangement of the actual scene. The basis of the behavior prediction of the target is that the target track and the corresponding behavior are deduced and predicted by learning the previous behavior of the human target or artificially.
Further, when the target enters a sensing area, the sensor information is stored, and when the target is sensed for 3 seconds continuously, the human target starts to be tracked; and when the human body target is not sensed to move continuously within 5 seconds, the target tracking is finished.
The invention solves the problem that the traditional infrared sensor network can only carry out field data acquisition; meanwhile, side-looking perception and overlooking perception are combined to realize advantage complementation, wherein the side-looking perception can more effectively obtain the position information of a target, the overlooking perception can greatly reduce the calculation amount, and the combination of the side-looking perception and the overlooking perception reduces the installation difficulty and the calculation difficulty of single overlooking perception and single side-looking perception; the traditional human body target tracking system is limited to only tracking a target, and the system can infer and predict the behavior of the human body target according to the tracking information and the actual scene of the target.
Drawings
FIG. 1 is a schematic diagram of the overall structure of an indoor human target tracking system according to the present invention;
FIG. 2 is a schematic view of an infrared sensor of the present invention;
FIG. 3 is a side view (top view) of a node sensing angle (area) according to the present invention;
FIG. 4 is a schematic diagram of a side view node profile generation of the present invention;
FIG. 5 is a schematic diagram of a side-view and top-view infrared sensor network perception model according to the present invention;
FIG. 6 is a schematic diagram of a two-dimensional arrangement of a side-view and top-view infrared sensor network sensing model according to the present invention;
FIG. 7 is a schematic diagram of a multi-target location method of the present invention;
fig. 8 is a schematic diagram of human target tracking and behavior prediction according to the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
Fig. 1 is a schematic view of the overall structure of the indoor human target tracking system, 11 is a schematic view of the information collection node composition, and 12 is a schematic view of the sink node composition. There may be more information collection nodes, and only one sink node. The information acquisition nodes comprise side view nodes and top view nodes. The side view node uses a pyroelectric infrared sensor to realize side sensing and is provided with an infrared optical reflection device, as shown in 21 and 22 in fig. 2, which are respectively a side view and a top view, wherein a dark part represents the pyroelectric infrared sensor, a light part represents the optical reflection device, and the combination of the two can realize a sensing range shown in 23 in fig. 2. The overlooking node uses an infrared sensor and is provided with a Fresnel lens to realize overlooking perception, as shown in 24 and 25 in figure 2, the perception angle shown in 26 in figure 2 can be adjusted by adjusting the shielding area of a dark color part shown in 25 in figure 2, and further the perception range is adjusted.
The sensing range of one sensor in the side looking node in combination with the infrared optical reflection means is indicated at 31 in fig. 3. The side view node adopts an upper layer and a lower layer, when 4 infrared sensors are arranged on each layer, the total sensing range can be larger than 180 degrees, the sensing range of the infrared sensors is thinned to 22.5 degrees after the sensing ranges of the infrared sensors are overlapped, and 32 degrees are shown in figure 3. The overlooking node uses 4 infrared sensors, each equipped with a fresnel lens to realize overlooking perception, dividing the ground area into 4 approximately concentric circles, as shown at 33 in fig. 3. When the human body target is sensed, the output of the corresponding sensor is 1, otherwise, the output is 0. The triangular area in fig. 3 represents a human body target, for the side view nodes, sensor sensing signals are sequentially output according to the sequence of 321-328, the output signal corresponding to 32 in fig. 3 is 0110-; for the look-down nodes, in order from inside to outside, the sensor output is 0011.
The information acquisition node sends the sensor output signal to the sink node and then transmits the sensor output signal to the upper computer. For each side-looking node, judging a first layer overlapping region according to the binarization output of a first layer sensor, judging a second layer overlapping region according to the binarization output of a second layer, and finally carrying out traffic operation on the two layers of overlapping regions to obtain a final refined sensing range; and for each overlooking node, directly judging the circular ring range of the target according to the output of the sensor.
For each side-looking node, after the sensing range of the target is judged, the corresponding measuring line needs to be activated. When the object is in the range shown at 41, 42, a diagonal line of the range is taken as a measuring line, and the extension line of the diagonal line passes through the corresponding side view node, as shown at 44 and 45 in fig. 4; when the human target is in the refined perception range shown at 43, the longer bisector is taken as the line of sight, as shown at 46 in FIG. 4.
The number of the information acquisition nodes which are actually arranged is determined by the size of the required total sensing range, and the condition that all detection areas of the sensors can cover all positions where targets can appear is taken as the criterion. In an indoor environment of 10m by 10m size, information collection can be performed by using 9 nodes, and the arrangement mode is shown in fig. 5. Shown at 51 is a top view node, and when the node is installed at a height of 3m, the radii of the 4 approximately circular sensing ranges are set to be 1m, 1.7m, 3m and 6m from inside to outside, respectively. 52. Side view nodes are shown at 53, 52 at the wall mid-position 1 meter high, 53 at the corners, and the side view nodes are placed in the same manner for the remaining three walls.
Fig. 6 is a schematic diagram of two-dimensional arrangement of a side-view and top-view infrared sensor network perception model of the present invention, wherein 61 is a top-view node, and the top-view schematic diagram is shown as 611 in fig. 6; 62-69 are side view nodes, a side view schematic diagram is shown at 621 in FIG. 6. The human body target is represented by a triangle in the figure, and the tracking steps corresponding to the human body target are as follows:
step 1: according to the output of the side-looking sensor nodes, the measuring lines of the infrared sensors of each side-looking node corresponding to the refined sensing range are activated, if nodes No. 63-69 in the figure 6 can sense a target, the corresponding measuring lines are respectively activated, a plurality of cross points can be generated among the measuring lines, the cross points are called primary measuring points, for a human body target, only part of the cross points can reflect the real position of the target, and the rest cross points are far away from the real target and are regarded as false measuring points to be removed;
step 2: according to the output of the overlooking sensor node, activating a region with a target, calling the region as a primary sensing domain, eliminating primary measuring points which do not exist in the primary sensing domain in the step 1, namely a-g measuring points marked in the figure 3, and reserving the rest measuring points and calling the remaining measuring points as secondary measuring points;
and step 3: if a secondary measuring point is in the thinning sensing range of the activated measuring line of a certain side-looking node, increasing the reliability of the secondary measuring point by 1, and if the secondary measuring point exceeds the total sensing range of the certain side-looking node, also increasing the reliability of the secondary measuring point by 1; for each secondary measuring point, traversing all side-looking nodes to respectively obtain the reliability of the side-looking nodes, so that each target has the secondary measuring point with the same highest reliability when multi-target tracking is realized; as shown in fig. 7, the line intersections b, d of the target 1 (triangle in the figure) can be detected by 4 sensors and are within the refined sensing range of the activated line, and the degree of reliability is 4, and the line intersections f, g of the target 2 (circle in the figure) can be detected by 3 sensors and are within the refined sensing range of the activated line, and the degree of reliability is 3, but since the line intersections f, g exceed the total sensing range of the sensor node 73, the degree of reliability is added with 1, so that the final degree of reliability of the target 2 is 4;
and 4, step 4: sorting the confidence degrees of the secondary measuring points in the step 3, wherein the measuring point with the highest confidence degree is called an effective measuring point, namely the b and d points, the f and g points obtained in the step 3, and then matching the effective measuring point with a corresponding target by using a multi-dimensional distribution method so as to obtain a positioning point of each target;
and 5: and smoothing the track by adopting a Kalman filtering tracking algorithm, and further matching the motion tracking track of the target with a target model to realize target tracking.
Fig. 8 is a schematic diagram of tracking and behavior prediction of human targets in the present invention. The k-th time position is a predicted position predicted from positions (points in the solid line in the figure) before the k-th time. The behavior of the target can be predicted based on the layout of the various pieces of furniture in the figure and the predicted position of the target at the next moment, as shown by the dotted line portion in the figure, where the most likely behavior of the target is the position of the walk-to sofa.
The above description is only illustrative of one application scenario of the present invention, and the scope of the present invention is not limited thereto, and any simple changes and equivalent substitutions made by those skilled in the art within the spirit scope of the present invention also belong to the scope of the present invention.
Claims (5)
1. The utility model provides an indoor human target tracking system of infrared sensor based on thing networking which characterized in that: designing an omnibearing indoor human body target perception model of a side-looking-down infrared sensor network, wherein the model comprises side-looking nodes and looking-down nodes, combining a sensor measuring line crossing method and detection area division through reasonable deployment of sensors, performing cooperative processing on human body target information collected by the side-looking nodes and the looking-down nodes to obtain position information of a human body target, and performing human body target positioning to further realize human body target tracking;
the human body target tracking comprises the following steps:
(1) according to the output of the side-looking sensor nodes, activating corresponding measuring lines, taking the positions of the sensor nodes as a reference, if the end points corresponding to the refined sensing regions just contain the reference positions, activating longer angle bisectors of the refined sensing regions as measuring lines, otherwise, making diagonal lines of the refined sensing regions just passing through the reference positions as measuring lines, regarding cross points generated between the measuring lines as primary measuring points, regarding a human body target, only part of the cross points can reflect the real position of the target, and the rest of the cross points are false measuring points to be removed;
(2) activating an area with a target according to the output of the overlooking sensor node, regarding the area as a primary sensing domain, eliminating the primary measuring points which do not exist in the primary sensing domain in the step (1), and reserving the rest measuring points to be used as secondary measuring points;
(3) if one secondary measuring point is located in a refined sensing range where a side-looking node activation measuring line is located, or the secondary measuring point exceeds the total sensing range of a side-looking node, increasing the reliability of the secondary measuring point; for each secondary measuring point, traversing all the side-looking nodes to obtain the reliability of the side-looking nodes;
(4) reserving the secondary measuring point with the highest intensity in the step (3) as an effective measuring point, and matching the effective measuring point with a corresponding target by using a multi-dimensional distribution method to further obtain a positioning point of each target;
(5) and smoothing the target track by adopting a Kalman filtering algorithm, and matching the motion tracking track of the target with a target model to realize target tracking.
2. The infrared sensor indoor human body target tracking system based on the Internet of things as claimed in claim 1, wherein a pyroelectric infrared sensor information acquisition node and a sink node based on a Contiki system are designed, the information acquisition node sends acquired human body target information to the sink node, and the sink node transmits the received information to an upper computer through a serial port or the Internet to perform on-site and remote tracking on a human body target.
3. The internet-of-things-based infrared sensor indoor human body target tracking system according to any one of claims 1 or 2, characterized in that a side-view and top-view infrared sensor network omnibearing target perception model is designed, wherein the side-view node adopts a double-layer design, a pyroelectric infrared sensor is adopted to be equipped with an infrared optical reflection device to realize side perception, the side-view node is arranged on a wall, the height from the ground is less than 1m, the perception range of the node is designed into an upper layer and a lower layer, each layer is provided with 4 infrared sensors, the perception angle of each infrared sensor is 90 degrees, and the perception range is refined after the perception ranges of different infrared sensors are overlapped to be 22.5 degrees; when the side view nodes are arranged at the corner positions, a total sensing range of 90 degrees is needed, in order to realize a larger sensing range, 3 infrared sensors on each layer are used, and 6 infrared sensors are used in total; overlook the node and hang on the ceiling or apart from ground 3m eminence, every node uses 4 infrared sensor, every infrared sensor all is equipped with fresnel lens and realizes overlooking the perception, divides into 4 approximate concentric circles with the ground region for to human target location and trail, look sideways at the node with overlook the adjustable number of node, it is accurate to cover all positions that the target appears in the detection area.
4. The internet-of-things-based infrared sensor indoor human body target tracking system as claimed in claim 3, wherein the target trajectory and corresponding behaviors are inferred and predicted by learning previous behaviors of the human body target or artificially according to the tracking information of the target, a motion model and the arrangement of an actual scene.
5. The internet-of-things-based infrared sensor indoor human body target tracking system according to claim 3, wherein when a target enters the sensing area, sensor information is stored, and when the target is sensed for 3 seconds continuously, the human body target is tracked; and when the human body target is not sensed to move continuously within 5 seconds, the target tracking is finished.
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| CN109855678A (en) * | 2018-12-28 | 2019-06-07 | 广东匠格装饰设计工程有限公司 | A kind of environmental monitoring system for the interior space |
| CN110554355A (en) * | 2019-08-01 | 2019-12-10 | 航天科工防御技术研究试验中心 | Target positioning system and method |
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| CN111148323B (en) * | 2019-12-25 | 2022-07-19 | 重庆特斯联智慧科技股份有限公司 | Intelligent building illuminating lamp light control method and system based on target perception |
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