# US 2010/0166259 A1 · Object enumerating apparatus and object enumerating method > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2010/0166259 A1 - Document ID: US20100166259A1 - Status: Published application - Kind: A1 - Application number: 12/377,734 - Filed: 2007-08-15 - Published: 2010-07-01 - Earliest claimed: 2006-08-17 - Inventors: Nobuyuki Otsu; Yasuyuki Shimohata - Web page: https://patentgrep.com/patent/US20100166259A1 ## CPC - G06V 20/52: Surveillance or monitoring of activities, e.g. for recognising suspicious objects (recognising microscopic objects G06V20/69) - G06V 10/443: {by matching or filtering} - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING ## Abstract An object enumerating apparatus comprises means for generating and binarizing inter-frame differential data from moving image data representative of a photographed object under detection, means for extracting feature data from a plurality of the inter-frame binary differential data directly adjacent to each other on a pixel-by-pixel basis through cubic higher-order local auto-correlation, means for calculating a coefficient of each factor vector from a factor matrix comprised of a plurality of factor vectors previously generated through learning using a factor analysis and arranged for one object under detection, and the feature data, and means for adding a plurality of the coefficients for one object under detection, and rounding off the sum to the decimal point to the closest integer representative of a quantity. By courtesy of small fluctuations in the sum of coefficients and accurate matching with the quantity of objects intended for recognition, a recognition can be accomplished with robustness to differences in scale and speed of objects and to dynamic changes thereof. ## Claims ### Claim 1 An object enumerating apparatus characterized by comprising: binarized differential data generating means for generating and binarizing inter-frame differential data from moving image data comprised of a plurality of image frame data representative of a photographed object under detection; feature data extracting means for extracting feature data from three-dimensional data comprised of a plurality of the inter-frame binary differential data directly adjacent to each other through cubic higher-order local auto-correlation; coefficient calculating means for calculating a coefficient of each factor vector from a factor matrix comprised of a plurality of factor vectors previously generated through learning and arranged for one object under detection, and the feature data; adding means for adding a plurality of the coefficients for one object under detection; and round-off means for rounding off an output value of said adding means to the decimal point to the closest integer representative of a quantity. ### Claim 2 (depends on 1) An object enumerating apparatus according to claim 1, characterized by further comprising learning means for generating a factor matrix based on feature data derived from learning data. ### Claim 3 (depends on 2) An object enumerating apparatus according to claim 2, characterized in that said learning means comprises: binarized differential data generating means for generating and binarizing inter-frame differential data from moving image data comprised of a plurality of image frame data representative of a photographed object under detection which comprises learning data; feature data extracting means for extracting feature data from three-dimensional data comprised of a plurality of the inter-frame binarized differential data through cubic higher-order local auto-correlation; and factor matrix generating means for generating a factor matrix from the feature data corresponding to a plurality of learning data through a factor analysis using a known quantity of objects in the learning data. ### Claim 4 (depends on 2) An object enumerating apparatus according to claim 2, characterized in that said plurality of factor vectors corresponding to one object under detection, included in the factor matrix, are generated respectively from a plurality of learning data which differ in at least one of a scale, a moving speed, and a moving direction of the object on a screen. ### Claim 5 An object enumerating apparatus characterized by comprising: binarized differential data generating means for generating and binarizing inter-frame differential data from moving image data comprised of a plurality of image frame data representative of a photographed object under detection; feature data extracting means for extracting feature data from three-dimensional data comprised of a plurality of the inter-frame binary differential data directly adjacent to each other through cubic higher-order local auto-correlation; learning means for generating a coefficient matrix for calculating the quantity of the object under detection based on feature data derived from a plurality of learning data which differ in at least one of a scale, a moving speed, and a moving direction of the object on a screen; quantity calculating means for calculating a quantity from a coefficient matrix previously generated by said learning means and the feature data derived from recognition data; and round-off means for rounding off an output value of said quantity calculating means to the decimal point to the closest integer. ### Claim 6 An object enumerating method characterized by comprising the steps of: generating a factor matrix based on cubic higher-order local auto-correlation, based on learning data; generating and binarizing inter-frame differential data from moving image data comprised of a plurality of image frame data representative of a photographed object under detection; extracting feature data from three-dimensional data comprised of a plurality of the inter-frame binary differential data directly adjacent to each other through cubic higher-order local auto-correlation; calculating a coefficient of each factor vector from a factor matrix comprised of a plurality of factor vectors previously generated through learning and arranged for one object under detection, and the feature data; adding a plurality of the coefficients for one object under detection; and rounding off an output value of said adding means to the decimal point to the closest integer representative of a quantity.