# US 2018/0025231 A1 · System and method for providing surveillance data > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2018/0025231 A1 - Document ID: US20180025231A1 - Status: Published application - Kind: A1 - Application number: 15/398,843 - Filed: 2017-01-05 - Published: 2018-01-25 - Earliest claimed: 2016-07-21 - Assignees: Hanwha Techwin Co., Ltd. - Applicants: Hanwha Techwin Co., Ltd. - Inventors: Seung In NOH; Jeong Eun LIM; Seoung Seon JEON - Web page: https://patentgrep.com/patent/US20180025231A1 ## CPC - G06V 10/431: {Frequency domain transformation; Autocorrelation} - G06F 18/22: Matching criteria, e.g. proximity measures - G06V 10/56: relating to colour - G06V 10/75: Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video features; Coarse-fine approaches, e.g. multi-scale approaches; using context analysis; Selection of dictionaries - G06V 20/41: {Higher-level, semantic clustering, classification or understanding of video scenes, e.g. detection, labelling or Markovian modelling of sport events or news items (segmenting video sequences G06V20/49)} - G06V 20/48: {Matching video sequences} - G06V 20/52: Surveillance or monitoring of activities, e.g. for recognising suspicious objects (recognising microscopic objects G06V20/69) - G06F 2218/08: Feature extraction - G06F 2218/12: Classification; Matching - G06V 20/44: {Event detection} - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract A system and method for providing surveillance data are provided. The system includes: a pattern learner configured to learn a time-based data pattern by analyzing at least one of image data of one or more images and sound data of sound obtained from a surveillance zone at a predetermined time or time period, and to generate an event model based on the time-based data pattern; and an event detector configured to detect at least one event by comparing the event model with a time-based data pattern of at least one of first image data of one or more first images and first sound data of first sound obtained from the surveillance zone. ## Claims ### Claim 1 A system for providing surveillance data, the system comprising at least one processor to implement: a pattern learner configured to learn a time-based data pattern by analyzing at least one of image data of one or more images and sound data of sound obtained from a surveillance zone at a predetermined time or time period, and to generate an event model based on the time-based data pattern; and an event detector configured to detect at least one event by comparing the event model with a time-based data pattern of at least one of first image data of one or more first images and first sound data of first sound obtained from the surveillance zone. ### Claim 2 (depends on 1) The system of claim 1, wherein the pattern learner comprises: a first learner configured to calculate a statistical data value of at least one of a color, a number of at least one object detected in the surveillance zone, and a degree of movement of the detected at least one object from the one or more images; and a second learner configured to calculate a statistical data value of at least one sound level from the sound, wherein the time-based data pattern corresponds to a time-based variation in the statistical value calculated by the first learner or the second learner. ### Claim 3 (depends on 2) The system of claim 2, wherein the pattern learner is configured to generate the event model based on the image data and the sound data corresponding to a first average value, of the at least one of the color, the number of the at least one object, and the degree of movement, and a second average value of the at least one sound level, calculated by the first learner and the second learner, respectively, and wherein, in response to a value of the first image data or a value of the first sound data being identical to or greater than the first average value or the second average value, respectively, the event detector is configured to determine that the at least one event has occurred. ### Claim 4 (depends on 1) The system of claim 1, wherein the event detector is configured to detect the at least one event from the first image data and the first sound data by considering daily data variation patterns of the image data and the sound data. ### Claim 5 (depends on 4) The system of claim 4, wherein in response to the first image data and the first sound data not corresponding to the daily data variation patterns, the event detector is configured to determine that the at least one event has occurred. ### Claim 6 (depends on 1) The system of claim 1, wherein the pattern learner is configured to determine a moving path of an object, detected in the surveillance zone, based on image data obtained from a plurality of cameras photographing the surveillance zone, and wherein the pattern learner is configured to generate the event model by additionally based on a time-based data pattern of the moving path. ### Claim 7 (depends on 1) The system of claim 1, wherein the event detector is further configured to assign priorities to the detected at least one event comprising a plurality of events, and wherein the system further comprises an event output interface configured to generate an event list of the detected events. ### Claim 8 (depends on 1) The system of claim 1, wherein the event detector is further configured to assign the priorities, different from one another, to the detected events based on an amount of difference between the event model and the time-based data pattern. ### Claim 9 (depends on 1) The system of claim 1, wherein the system further comprises or is connected to a database storing the first image data and the first sound data, and wherein the pattern learner is configured to receive a user's search request and update the event model according to the user's search request. ### Claim 10 (depends on 9) The system of claim 9, wherein the event detector is further configured to assign priorities to the detected at least one event comprising a plurality of events, wherein the system further comprises an event output interface configured to generate an event list of the detected events, and wherein the event output interface is configured to search for data corresponding to the user's search request in the database and add the data to the event list. ### Claim 11 A method of providing surveillance data, the method comprising: learning a time-based data pattern by analyzing at least one of image data of one or more images and sound data of sound obtained from a surveillance zone at a predetermined time or time period, and to generate an event model based on the time-based data pattern; and detecting at least one event by comparing the event model with a time-based data pattern of at least one of first image data of one or more first images and first sound data of first sound obtained from the surveillance zone. ### Claim 12 (depends on 11) The method of claim 11, wherein the learning the time-based data pattern comprises: performing a first learning operation to calculate a statistical data value of at least one of a color, a number of at least one object detected in the surveillance zone, and a degree of movement of the detected at least one object from the one or more images; and performing a second learning operation to calculate a statistical data value of at least one sound level from the sound, wherein the time-based data pattern corresponds to a time-based variation in the statistical value calculated by the first learner or the second learner. ### Claim 13 (depends on 12) The method of claim 12, wherein in the learning the time-based data patterns, the event model is generated based on the image data and the sound data corresponding to a first average value, of the at least one of the color, the number of the at least one object, and the degree of movement, and a second average value of the at least one sound level, calculated through the first learning operation and the second learning operation, and wherein in the detecting the at least one event, it is determined that the at least one event has occurred, in response to a value of the first image data or a value of the first sound data being identical to or greater than the first average value or the second average value, respectively. ### Claim 14 (depends on 11) The method of claim 11, wherein in the detecting the at least one event, the at least one event is detected from the first image data and the first sound data by considering daily data variation patterns of the image data and the sound data. ### Claim 15 (depends on 14) The method of claim 14, wherein the first image data and the first sound data not corresponding to the daily data variation patterns, it is determined that the at least one event has occurred. ### Claim 16 (depends on 11) The method of claim 11, further comprising determining a moving path of an object, detected in the surveillance zone, based on image data obtained from a plurality of cameras photographing the surveillance zone, wherein the event model is generated by additionally based on a time-based data pattern of the moving path. ### Claim 17 (depends on 11) The method of claim 11, further comprising: assigning priorities to the detected at least one event comprising a plurality of events; and generating an event list of the detected events. ### Claim 18 (depends on 11) The method of claim 11, wherein the assigning the priorities comprises assigning the priorities, different from one another, to the detected events based on an amount of difference between the event model and the time-based data pattern. ### Claim 19 (depends on 11) The method of claim 11, further comprising: storing the first image data and the first sound data in a database; and receiving a user's search request and updating the event model according to the user's search request. ### Claim 20 (depends on 19) The method of claim 19, further comprising: assigning priorities to the detected at least one event comprising a plurality of events; generating an event list of the detected events; and searching for data corresponding to the user's search request in the database and adding the data to the event list.