# US 2022/0284689 A1 · Computer software module arrangement, a circuitry arrangement, an arrangement and a method for improved object detection > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2022/0284689 A1 - Document ID: US20220284689A1 - Status: Published application - Kind: A1 - Application number: 17/625,390 - Filed: 2019-07-17 - Published: 2022-09-08 - Earliest claimed: 2019-07-17 - Assignees: Telefonaktiebolaget LM Ericsson (publ) - Applicants: Telefonaktiebolaget LM Ericsson (publ) - Inventors: Ashkan KALANTARI; Héctor CALTENCO; Saeed BASTANI; Yun LI - Web page: https://patentgrep.com/patent/US20220284689A1 ## CPC - G06V 10/95: {structured as a network, e.g. client-server architectures} - G06F 18/253: {of extracted features} - G06V 10/17: {using hand-held instruments} - G06V 10/454: {Integrating the filters into a hierarchical structure, e.g. convolutional neural networks [CNN]} - G06V 10/82: using neural networks - G06V 20/52: Surveillance or monitoring of activities, e.g. for recognising suspicious objects (recognising microscopic objects G06V20/69) - G06V 10/16: {using multiple overlapping images; Image stitching} - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract An object detection arrangement having a controller configured to: a) receive a plurality of image data streams; b) perform feature extraction on each of the received a plurality of images providing a plurality of feature data streams; and to c) perform a common feature extraction based on the plurality of feature data streams providing as common feature data stream for object detection. ## Claims ### Claim 1 An object detection arrangement comprising a controller configured to: receive a plurality of image data streams; perform feature extraction on each of the received plurality of images providing a plurality of feature data streams; and perform a common feature extraction based on the plurality of feature data streams providing a common feature data stream for object detection. ### Claim 2 (depends on 1) The object detection arrangement according to claim 1, wherein the controller is further configured to perform object detection based on the common feature data stream. ### Claim 3 (depends on 2) The object detection arrangement according to claim 2, wherein the object detection is based on a Deep Neural Network model. ### Claim 4 (depends on 1) The object detection arrangement according to claim 1, wherein the controller is further configured to apply a plurality of neural network layers exhibiting temporal dynamic behavior to the plurality of feature data streams, providing the common feature data stream. ### Claim 5 (depends on 1) The object detection arrangement according to claim 1, wherein the controller is further configured to perform the common feature extraction based on a neural network exhibiting temporal dynamic behavior. ### Claim 6 (depends on 5) The object detection arrangement according to claim 5, wherein the neural network exhibiting temporal dynamic behavior is a recurrent neural network (RNN). ### Claim 7 (depends on 1) The object detection arrangement according to claim 1, wherein one image data stream comprises images at least partially overlapping images in another image data stream. ### Claim 8 (depends on 1) The object detection arrangement according to claim 1, further comprising a plurality of image capturing devices, wherein each image data stream originates from one of the image capturing devices. ### Claim 9 (depends on 8) The object detection arrangement according to claim 8, wherein at least one of the image capturing device is configured to perform the feature extraction on the corresponding image data stream. ### Claim 10 (depends on 1) The object detection arrangement according to claim 1, wherein the controller is further configured to combine the plurality of feature data streams into a combined feature data stream. ### Claim 11 (depends on 1) The object detection arrangement according to claim 1, wherein the object detection arrangement is a one of smartphone and a tablet computer. ### Claim 12 (depends on 1) The object detection arrangement according to claim 1, wherein the object detection arrangement is an optical see-through device. ### Claim 13 (depends on 1) The object detection arrangement according to claim 1, wherein the object detection arrangement is arranged to be used in image retrieval, industrial use, robotic vision and/or video surveillance. ### Claim 14 A method for object detection in an object detection arrangement, the method comprising: receiving a plurality of image data streams; performing feature extraction on each of the received plurality of images providing a plurality of feature data streams; and performing a common feature extraction based on the plurality of feature data streams providing a common feature data stream for object detection. ### Claim 15 A computer-readable storage medium storing computer instructions that when loaded into and executed by a controller of an object detection arrangement causes the object detection arrangement to perform a method, the method comprising: receiving a plurality of image data streams; performing feature extraction on each of the received plurality of images providing a plurality of feature data streams; and performing a common feature extraction based on the plurality of feature data streams providing a common feature data stream for object detection. ### Claim 16 (canceled) ### Claim 17 (canceled) ### Claim 18 (depends on 2) The object detection arrangement according to claim 2, wherein the controller is further configured to apply a plurality of neural network layers exhibiting temporal dynamic behavior to the plurality of feature data streams, providing the common feature data stream. ### Claim 19 (depends on 2) The object detection arrangement according to claim 2, wherein the controller is further configured to perform the common feature extraction based on a neural network exhibiting temporal dynamic behavior. ### Claim 20 (depends on 19) The object detection arrangement according to claim 19, wherein the neural network exhibiting temporal dynamic behavior is a recurrent neural network (RNN). ### Claim 21 (depends on 2) The object detection arrangement according to claim 2, wherein one image data stream comprises images at least partially overlapping images in another image data stream. ### Claim 22 (depends on 2) The object detection arrangement according to claim 2, further comprising a plurality of image capturing devices, wherein each image data stream originates from one of the image capturing devices.