US20150051719A1 - Apparatus and Method for Analyzing Sensor Data - Google Patents

Apparatus and Method for Analyzing Sensor Data Download PDF

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US20150051719A1
US20150051719A1 US14/355,109 US201114355109A US2015051719A1 US 20150051719 A1 US20150051719 A1 US 20150051719A1 US 201114355109 A US201114355109 A US 201114355109A US 2015051719 A1 US2015051719 A1 US 2015051719A1
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event
primitive
stream
sensor data
events
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Christopher Mutschler
Norbert Franke
Daniel Wolf
Nicolas Witt
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Fraunhofer Gesellschaft zur Forderung der Angewandten Forschung eV
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63F—CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR
    • A63F9/00—Games not otherwise provided for
    • A63F9/24—Electric games; Games using electronic circuits not otherwise provided for
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/131—Protocols for games, networked simulations or virtual reality
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/50—Network services
    • H04L67/52—Network services specially adapted for the location of the user terminal

Definitions

  • Embodiments of the present invention generally relate to the analysis of sensor data and, in particular, to apparatuses and methods for analyzing sensor data being provided at a high sensor data rate.
  • Wireless sensor networks of various technologies may be used for locating purposes, such as locating or tracking humans and/or other objects.
  • locating means the detection or determination of a geographical location or position
  • tracking means locating at short time intervals such as to be able to follow the position and other kinematic variables of a moving observed object.
  • Some specialized locating or position tracking systems may be used for locating players and other objects (e.g. a ball) in sport events, such as, for example, soccer, American football, rugby, tennis, etc.
  • location information may be determined based on the evaluation of wireless radio signals and/or magnetic fields.
  • transmitters and/or receivers generally also denoted as sensors, may be placed at the individual objects (e.g. players, ball, etc.) to be located by the system.
  • Corresponding reception and/or transmission devices may also be mounted to predetermined locations around a geographical area of interest, as e.g. a soccer field.
  • An evaluation of signal strengths, signal propagation times, and/or signal phases may then lead to sensor data streams indicative of the geographic position or location of individual players or objects at different time instants.
  • a location data sample may be associated with a time stamp indicating at which time an object was located at the observed or detected geographic position or location.
  • kinematic data like velocity (speed), acceleration, etc. may as well be provided in addition to the location data comprising, for example, x-, y-, and z-coordinates.
  • the location and/or kinematic data delivered by the localization sensor system will also be referred to as raw (sensor) data.
  • people or objects may be equipped with tiny transmitters, which may be embedded in footwear, uniforms and balls and whose signals are picked up by a number of antennas placed around an area under observation as for example a soccer field.
  • Receiver units process the collected signals and determine their Times of Arrival (ToA). Based on a calculation of the differences in propagation delay, each transmitter's position is determined continuously to track all objects under observation.
  • the transmitters operate in the 2.4 GHz band to be globally license free, while the individual transmitters are distinguished using CDMA (Code Division Multiple Access) techniques.
  • CDMA Code Division Multiple Access
  • tracking an object in a sports game with an appropriate accuracy easily produces a large amount of sensor data.
  • tracking a ball of a soccer game with an accuracy of roughly 3 cm may require to estimate the position of the ball up around 2000 times per second, assuming a maximum speed of the ball of about 150 km/h, which appears to be reasonable for soccer.
  • other sports such as tennis or baseball certainly would require higher sampling or estimation rates.
  • not only the ball is to be tracked in a soccer game where tracking the 22 players is equally important. Since tracking with the above mentioned system is principally feasible with an accuracy of 1 cm, it may also be desirable to use more than one tracking sensor per player, to not only derive his position but also his posture in order to be able to decide on offside situations or the like. Equipping two teams and the necessary play equipment for a soccer match with a reasonable amount of sensors may result in up to 50000 position data samples or raw data samples per second that is the associated sensor data stream may have a sensor data rate of 50000 per second.
  • Analyzing such type of sensor data requires simultaneous knowledge on the raw data of multiple or all sensors at each decision making, since each incident to be analyzed or since each conclusion to be drawn requires knowledge on a different subset of the sensors raw data. That is, in a conventional analysis framework all data needs to be distributed to all analyzing entities in parallel, which is not feasible if the number of analyzing entities increases in order to analyze the data with respect to multiple aspects. To even accomplish this task in real time or close to real time is completely out of reach in those systems. However, this may be required in order to indicate offside positions in a soccer game or to answer similar time critical questions in the course of another sports game.
  • an analysis framework for analyzing the raw data portions being transmitted in a sensor data stream at a sensor data rate.
  • the analysis framework comprises an input layer comprising at least two or more primitive event detectors for generating at least a first and second stream of primitive events using exclusively the sensor data stream as an input and one or more event layers comprising one or more event detectors for detecting an event of interest, wherein any one of the one or more event detectors uses only the streams of the first and the second primitive events or a stream of other events as provided by another one of the event detectors as an input. That is, the analysis framework follows an event based hierarchical analysis approach, where interdependent events are determined or detected at different hierarchical levels of dependency.
  • An event may generally be defined to be an instantaneous occurrence of interest at a point of time.
  • an event is associated with a change of a related sensed quantity.
  • An event may be a primitive event derived in the hierarchical base or input layer, such that a primitive event is directly based on sensor data (e.g. the kinematic data provided by a tracking system) and hence may indicate a change of the related sensed quantity.
  • An event of interest is an event of a higher hierarchical order that is it is dependent on other events or primitive events, or more generally on previously detected other events.
  • an event or a primitive event comprises information derived by applying a certain decision making criterion to another event or to the raw data.
  • An event based approach may provide for the possibility to analyze the underlying data in a parallelized manner with multiple processing nodes communicating over a network. This is particularly the case since the sensor data stream with its high data rate only needs to be provided to the primitive event detectors in the input layer, where they are processed to determine primitive events which are typically determined with a lower data rate. For the higher layers, primitive events are the basis for the analysis such that the sensor data stream does not have to be distributed to all decision making entities or event detectors. This makes the analysis of larger sets of position data at all possible.
  • the reduced data rate of the input events for the event detectors in the higher hierarchy layers provides for the possibility to parallelize the processing, for example by distributing the event detectors and/or the primitive event detectors to different CPUs or computers within an analysis framework consisting of several CPUs or computing nodes.
  • the reduced event data rates even provide for the possibility to communicate the occurrence of events via conventional networks, such as for example over an Ethernet based LAN (Local Area Network) without limiting the performance of the analysis framework even when a close to real time analysis is required.
  • Ethernet based LAN Local Area Network
  • the exchanged event data may also comprise information about two, three or more possible states the event detector has determined. That is, a determined event or primitive event may contain more than a binary YES/NO information.
  • an apparatus for detecting an event of interest comprising a first primitive event detector for generating a stream of first primitive events using the sensor data stream and a second primitive event detector for generating a stream of different second primitive events using the sensor data stream, wherein a data rate of each of the streams of the first primitive events and the second primitive events is lower than the sensor data rate.
  • An event detector is operable to determine the event of interest using the streams of the first and second primitive events. An event detector can thus base its decisions on primitive events occurring at a lower data rate and thus perform its analysis without the need to access the high data rate sensor data stream directly.
  • data rate is to be understood not only as an instantaneous data rate but also as a mean data rate, which is as an average of the instantaneous data rate over a certain time interval.
  • a certain primitive event is determined with the sensor data rate for a short time period.
  • the mean data rate of the primitive event over the time of the observation e.g. the time of a soccer match, may be much lower.
  • the primitive event detectors are operable to exclusively use kinematic data as the raw data in order to provide real time analysis of tracking data for sports events or the like.
  • Kinematic data may be an arbitrary subset of position information, velocity information or acceleration information of an observed object.
  • the data may be provided in a 1-, 2- or 3-dimensional space, such that the kinematic variables used herein shall be understood as being either vectors or scalars, depending on the chosen implementation.
  • the primitive event detectors may furthermore derive velocity- and/or acceleration information themselves from the provided position information, such that the sensors may be simple tracking or positioning devices or the like.
  • the hierarchical analysis is based on an appropriate choice of base events, such as to be able to derive any basic information needed for all higher order decisions.
  • a proximity-event is generated as a primitive event when position information of a first observed object and of a second observed object indicates that the second observed object is within a predetermined neighborhood of the first observed object.
  • the neighborhood may, depending on the observed event or sports game, be a complex shaped volume or be defined by the distance between the first observed object and the second observed object.
  • Proximity may be used as an input for numerous further event detections, as for example to answer the question whether a player/batter or other equipment was possibly hitting a ball or another observed object.
  • a remote-event is generated as a primitive event when position information of a first observed object and of a second observed object indicate that the second observed object is outside of a predetermined neighborhood of the first observed object.
  • a direction changed-event is generated as a primitive event when a change of a velocity (direction or quantity) or an acceleration of an observed object exceeds a predetermined threshold.
  • This criterion may generally be useful in sports- or tracking applications.
  • the height of the threshold may be different. The threshold may therefore be different for different kinds of sports or even varying during the course of a single game to account for changing environmental conditions, such as for example the occurrence of rain.
  • an idle-event is generated as a primitive event, when the velocity of an observed object falls below a predetermined threshold to indicate that the object is passive or not moving.
  • the idle event is only generated when the velocity of the observed object stays below the predetermined threshold for at least a predetermined minimum duration.
  • an active-event is generated as a primitive event, when a velocity of an observed object exceeds a predetermined threshold.
  • the active-event is only generated when the velocity of the observed object stays above the predetermined threshold for at least a predetermined minimum duration.
  • a line crossing-event is generated as a primitive event, when at least one component of a position information of an observed object exceeds or falls below a predetermined threshold.
  • FIG. 1 shows a schematic view of an analysis framework for analyzing sensor data streams
  • FIG. 2 schematically shows a block diagram of an embodiment of a method for detecting an event of interest using a sensor data stream
  • FIG. 3 schematically shows an example for an event detection scheme in order to detect a shot on a goal in a soccer game
  • FIG. 4 schematically shows an example for an event detection scheme in order to detect a double pass in a soccer game
  • FIG. 5 schematically shows events allowing to efficiently determine the presence of an observed object in a predetermined area
  • FIG. 6 schematically shows an embodiment of an event detector.
  • FIG. 1 schematically shows an analysis framework 2 for analyzing raw data portions being transmitted in a sensor data stream 4 at a sensor data rate to identify at least one event of interest.
  • the raw data portion is associated to one or more properties determined for an observed object using one or more sensors.
  • the raw data comprise only kinematic variables of observed players and of a ball of a soccer match.
  • An input layer 6 of the framework 2 comprises several primitive event detectors 8 a - 8 e for generating a stream of primitive events using exclusively the sensor data stream 4 as an input. That is, only the primitive event detectors 8 a - 8 e of the input layer of the analysis framework 2 receive the raw data at the high sensor data rate as an input.
  • a first primitive event detector 8 b and a second primitive event detector 8 c generate a first stream of primitive events 10 b and a second stream of primitive events 10 c using exclusively the sensor data stream 4 as an input.
  • the sensor data stream 4 may be provided using any suitable technique such as an Ethernet Network using copper or fiber links or any other wired or wireless technology, using standardized or custom communication protocols.
  • the analysis framework 2 comprises two event layers 12 and 14 , each comprising event detectors for detecting events of interest, wherein the event detectors of the event layers 12 and 14 use only the streams provided by the primitive event detectors or of other event detectors of event layers 12 or 14 as an input.
  • event detector 16 determines an event of interest using the streams 10 b and 10 c of the first and second primitive event detectors 8 b and 8 c .
  • Stream 10 b is also provided to a further event detector 18 . It goes without saying that further arbitrary interdependencies are possible. For the sake of clarity, however, only few dependencies or connections are illustrated here.
  • the links between the event detectors may be implemented using wired Ethernet connections or using any other network link based on an arbitrary technology known in the art.
  • the individual event detectors may be running as different software processes on different computing nodes or some of them may be running on the same node.
  • the event detectors may also be implemented as dedicated or programmable hardware or the like. With this respect, the dependencies between the event detectors shown in FIG. 1 shall be understood as both, a representation for a physical hardware link or for a logical link indicating a flow of information or a dependency of on event detector from another.
  • FIG. 2 schematically shows an embodiment of a method for detecting an event of interest using a sensor data stream 4 carrying raw data at a sensor data rate, the raw data being associated to one or more properties determined for multiple observed objects using one or more sensors.
  • a stream of first primitive events 22 is generated, wherein a data rate of the stream of first primitive events 22 is lower than the sensor data rate.
  • a stream of second primitive events 26 is generated, wherein a data rate of the stream of the second primitive events 26 is lower than the sensor data rate.
  • an event generation step 28 the event of interest 30 is determined, using the streams 22 and 26 of the first and second primitive events. It is to be noted that the first and second event generation steps 20 and 24 may be performed in any arbitrary order, that is the particular events of the of first second stream of primitive events which are used to determine an event of interest 30 do not necessarily have to be created or delivered at the same time.
  • event detectors may, for example, be implemented as state machines as it will be shown in FIG. 6 below.
  • FIGS. 3 to 5 discussed below show some embodiments of appropriately chosen primitive events to be used to derive higher order events of interest for a soccer match.
  • FIGS. 3 and 4 show examples of analysis frameworks for analyzing position information of players and a ball in a soccer game to identify numerous events of interest. It may be noted that the illustrative embodiments of FIGS. 3 and 4 may not show all interrelations and links between the event detectors or the decision making entities of the individual hierarchical levels. FIGS. 3 and 4 are mainly intended to clarify the hierarchical approach and particularly how few base events may be used to analyze the data to derive rather complex information from few primitive events.
  • FIG. 3 shows an example of an analysis framework for analyzing position information of players and a ball in a soccer game to identify a blocked shot on a the goal as an event of interest.
  • a “direction changed-event” 40 is detected for the ball, for example by comparing a provided acceleration with a threshold or by computing first and second derivatives of position data of the ball in order to conclude on the change of direction of the ball.
  • a line crossing event 42 is generated or derived, indicating that the ball had crossed a predetermined virtual line on the playfield.
  • the ball is identified to be in a particular region, generating a region event 44 .
  • the identification of a particular region is elaborated on in more detail in the description of FIG. 5 .
  • the observed ball is the active ball 46 , which is the ball actively used for playing and not a spare ball stored outside of the playfield.
  • Is near events 48 for player and ball as well as for ball and goal allow to draw the conclusion that the direction change of the ball indicated by the direction changed-event 40 was due to a particular player who hit the ball, which is signaled by a corresponding event 50 .
  • the shot was directed to the goal by event 52 .
  • Additional subsequent is near events for the ball and the goal keeper further allow concluding that the shot was blocked and that the no goal was achieved by the player hitting the ball.
  • the final complex information is gathered from only few but appropriately chosen primitive events.
  • FIG. 4 shows an example of an analysis framework for analyzing position information of players and a ball in a soccer game to identify a double pass between two players.
  • the determination which particular player hit the ball is performed as already explained with respect to FIG. 3 .
  • a change of the sign of a velocity or of a component of a velocity vector of the ball may be used to determine a peak height event 56 for the ball, which may indicate that the ball had been shot and not only dribbled.
  • the Begin and End of possession 58 of the ball may be determined from the proximity event 48 , indicating that the ball is close to a particular player.
  • a pass 62 can be determined by using the information that one player lost possession, while another player achieved possession of the ball, while the ball has been played in between, which in turn is detected by the height event 56 .
  • a double pass 64 may be determined by 2 subsequent passes between the same players.
  • FIG. 5 illustrates how the information that a ball 72 is located within a particular region 74 of the playfield may be gathered from just one type of primitive events in a particularly efficient manner.
  • the underlying primitive event is a line crossing event indicating that the ball has crossed a straight line virtually projected on the playfield in order to subdivide it into numerous regions.
  • the region 72 is formed by 4 straight lines 76 to 82 , intersecting at right angles such as to form a rectangular region 74 .
  • this may be practically achieved by determining, whether a component of a position vector of the ball exceeds or falls below a predetermined threshold.
  • the determination, whether a line has been crossed or not may then require different calculations, such as for example the determination of a determinate.
  • the determination of the position of the ball may be based on a primitive “line crossing-event” determined for each of the lines used to subdivide a playfield into multiple regions, as for example lines 76 to 80 , which provide the border of region 74 .
  • a line crossing event or a relative position event is determined, when the ball crosses the line.
  • the ball changes its position from a previous position 84 to its present position in region 74 . That is, a primitive event detector associated to line 82 would change its output state or, in other words, generate a line crossing-event.
  • an event generator receiving this event and in addition thereto the events determined by the primitive event detectors associated to lines 76 , 78 and 80 will be able to generate an event indicating that ball 72 is now in region 74 .
  • the event detector could, for example, be implemented as a state machine, as shown in more detail in FIG. 6 .
  • the computational load to determine the position of the ball with respect to predetermined regions, as for example with respect to the penalty area may decrease by a considerable amount.
  • the detection may then be performed as follows:
  • FIG. 6 schematically illustrates a possible implementation of an event detector 100 as a state machine.
  • the state machine is depending on 4 input events or input quantities A, B, C, and D, which may, for example, be provided by one or more primitive event detectors.
  • A, B, C, and D which may, for example, be provided by one or more primitive event detectors.
  • the state machine arrives at state 102 the state machine or the event detector generates or determines its associated event of interest, which naturally depends on the inputs to the event detector.
  • the state machine is initialized with its starting configuration 104 .
  • the state machine transits into state 106 from where it transits to state 108 upon occurrence of event B.
  • event C the state machine transits to its final state 102 and determines its associated event.
  • Further events of interest may be determined on the basis of the primitive events. Further Events of interest may, for example, be single passes from one player to another, double passes, shots on the goal, dribblings, free kicks, kick-offs or corner kicks.
  • Functional blocks denoted as “means for . . . ” shall be understood as functional blocks comprising circuitry that is adapted for performing a certain function, respectively.
  • a “means for s.th.” may as well be understood as a “means being adapted or operable for s.th.”.
  • a means being adapted for performing a certain function does, hence, not imply that such means necessarily is performing said function (at a given time instant).
  • processor any functional blocks may be provided through the use of dedicated hardware, as e.g. a processor, as well as hardware capable of executing software in association with appropriate software.
  • the functions may be provided by a single dedicated processor, by a single shared processor, or by a plurality of individual processors, some of which may be shared.
  • explicit use of the term “processor” or “controller” should not be construed to refer exclusively to hardware capable of executing software, and may implicitly include, without limitation, digital signal processor (DSP) hardware, network processor, application specific integrated circuit (ASIC), field programmable gate array (FPGA), read only memory (ROM) for storing software, random access memory (RAM), and non-volatile storage.
  • DSP digital signal processor
  • ASIC application specific integrated circuit
  • FPGA field programmable gate array
  • ROM read only memory
  • RAM random access memory
  • non-volatile storage Other hardware, conventional and/or custom, may also be included.
  • any block diagrams herein represent conceptual views of illustrative circuitry embodying the principles of the invention.
  • any flow charts, flow diagrams, state transition diagrams, pseudo code, and the like represent various processes which may be substantially represented in computer readable medium and so executed by a computer or processor, whether or not such computer or processor is explicitly shown.
  • each claim may stand on its own as a separate embodiment. While each claim may stand on its own as a separate embodiment, it is to be noted that—although a dependent claim may refer in the claims to a specific combination with one or more other claims—other embodiments may also include a combination of the dependent claim with the subject matter of each other dependent claim. Such combinations are proposed herein unless it is stated that a specific combination is not intended. Furthermore, it is intended to include also features of a claim to any other independent claim even if this claim is not directly made dependent to the independent claim. Particularly, when a dependent claim is referring to an encoder or a sender, the corresponding feature of the related decoder or receiver shall herewith also be included and part of the disclosure of the description.
  • a single step may include or may be broken into multiple substeps. Such substeps may be included and part of the disclosure of this single step unless explicitly excluded.

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BR112014010332A2 (pt) 2017-04-18
CN104025537A (zh) 2014-09-03
JP5970074B2 (ja) 2016-08-17
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