Patents by Inventor Ishani Chakraborty
Ishani Chakraborty has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Publication number: 20240078682Abstract: Training a multi-object tracking model includes: generating a plurality of training images based at least on scene generation information, each training image comprising a plurality of objects to be tracked; generating, for each training image, original simulated data based at least on the scene generation information, the original simulated data comprising tag data for a first object; locating, within the original simulated data, tag data for the first object, based on at least an anomaly alert (e.g., occlusion alert, proximity alert, motion alert) associated with the first object in the first training image; based at least on locating the tag data for the first object, modifying at least a portion of the tag data for the first object from the original simulated data, thereby generating preprocessed training data from the original simulated data; and training a multi-object tracking model with the preprocessed training data to produce a trained multi-object tracker.Type: ApplicationFiled: November 13, 2023Publication date: March 7, 2024Inventors: Ishani CHAKRABORTY, Jonathan C. HANZELKA, Lu YUAN, Pedro Urbina ESCOS, Thomas M. SOEMO
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Patent number: 11880985Abstract: The disclosure herein enables tracking of multiple objects in a real-time video stream. For each individual frame received from the video stream, a frame type of the frame is determined. Based on the individual frame being an object detection frame type, a set of object proposals is detected in the individual frame, associations between the set of object proposals and a set of object tracks are assigned, and statuses of the set of object tracks are updated based on the assigned associations. Based on the individual frame being an object tracking frame type, single-object tracking is performed on the frame based on each object track of the set of object tracks and the set of object tracks is updated based on the performed single-object tracking. For each frame received, a real-time object location data stream is provided based on the set of object tracks.Type: GrantFiled: May 28, 2022Date of Patent: January 23, 2024Assignee: Microsoft Technology Licensing, LLC.Inventors: Ishani Chakraborty, Yi-Ling Chen, Lu Yuan
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Patent number: 11854211Abstract: Training a multi-object tracking model includes: generating a plurality of training images based at least on scene generation information, each training image comprising a plurality of objects to be tracked; generating, for each training image, original simulated data based at least on the scene generation information, the original simulated data comprising tag data for a first object; locating, within the original simulated data, tag data for the first object, based on at least an anomaly alert (e.g., occlusion alert, proximity alert, motion alert) associated with the first object in the first training image; based at least on locating the tag data for the first object, modifying at least a portion of the tag data for the first object from the original simulated data, thereby generating preprocessed training data from the original simulated data; and training a multi-object tracking model with the preprocessed training data to produce a trained multi-object tracker.Type: GrantFiled: January 26, 2022Date of Patent: December 26, 2023Assignee: Microsoft Technology Licensing, LLC.Inventors: Ishani Chakraborty, Jonathan C. Hanzelka, Lu Yuan, Pedro Urbina Escos, Thomas M. Soemo
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Patent number: 11450019Abstract: A computing system is configured to train an object classifier. Monocular image data and ground-truth data are received for a scene. Geometric context is determined including a three-dimensional camera position relative to a fixed plane. Regions of interest (RoI) and a set of potential occluders are identified within the image data. For each potential occluder, an occlusion zone is projected onto the fixed plane in three-dimensions. A set of occluded RoIs on the fixed plane are generated for each occlusion zone. Each occluded RoI is projected back to the image data in two-dimensions. The classifier is trained by minimizing a loss function generated by inputting information regarding the RoIs and the occluded RoIs into the classifier, and by minimizing location errors of each RoI and each occluded RoI of the set on the fixed plane based on the ground-truth data. The trained classifier is then output for object detection.Type: GrantFiled: October 22, 2020Date of Patent: September 20, 2022Assignee: Microsoft Technology Licensing, LLCInventors: Ishani Chakraborty, Gang Hua
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Publication number: 20220292828Abstract: The disclosure herein enables tracking of multiple objects in a real-time video stream. For each individual frame received from the video stream, a frame type of the frame is determined. Based on the individual frame being an object detection frame type, a set of object proposals is detected in the individual frame, associations between the set of object proposals and a set of object tracks are assigned, and statuses of the set of object tracks are updated based on the assigned associations. Based on the individual frame being an object tracking frame type, single-object tracking is performed on the frame based on each object track of the set of object tracks and the set of object tracks is updated based on the performed single-object tracking. For each frame received, a real-time object location data stream is provided based on the set of object tracks.Type: ApplicationFiled: May 28, 2022Publication date: September 15, 2022Inventors: Ishani CHAKRABORTY, Yi-Ling CHEN, Lu YUAN
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Patent number: 11386662Abstract: The disclosure herein enables tracking of multiple objects in a real-time video stream. For each individual frame received from the video stream, a frame type of the frame is determined. Based on the individual frame being an object detection frame type, a set of object proposals is detected in the individual frame, associations between the set of object proposals and a set of object tracks are assigned, and statuses of the set of object tracks are updated based on the assigned associations. Based on the individual frame being an object tracking frame type, single-object tracking is performed on the frame based on each object track of the set of object tracks and the set of object tracks is updated based on the performed single-object tracking. For each frame received, a real-time object location data stream is provided based on the set of object tracks.Type: GrantFiled: May 28, 2020Date of Patent: July 12, 2022Assignee: Microsoft Technology Licensing, LLCInventors: Ishani Chakraborty, Yi-Ling Chen, Lu Yuan
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Patent number: 11335008Abstract: Training a multi-object tracking model includes: generating a plurality of training images based at least on scene generation information, each training image comprising a plurality of objects to be tracked; generating, for each training image, original simulated data based at least on the scene generation information, the original simulated data comprising tag data for a first object; locating, within the original simulated data, tag data for the first object, based on at least an anomaly alert (e.g., occlusion alert, proximity alert, motion alert) associated with the first object in the first training image; based at least on locating the tag data for the first object, modifying at least a portion of the tag data for the first object from the original simulated data, thereby generating preprocessed training data from the original simulated data; and training a multi-object tracking model with the preprocessed training data to produce a trained multi-object tracker.Type: GrantFiled: September 18, 2020Date of Patent: May 17, 2022Assignee: Microsoft Technology Licensing, LLCInventors: Ishani Chakraborty, Jonathan C. Hanzelka, Lu Yuan, Pedro Urbina Escos, Thomas M. Soemo
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Publication number: 20220148197Abstract: Training a multi-object tracking model includes: generating a plurality of training images based at least on scene generation information, each training image comprising a plurality of objects to be tracked; generating, for each training image, original simulated data based at least on the scene generation information, the original simulated data comprising tag data for a first object; locating, within the original simulated data, tag data for the first object, based on at least an anomaly alert (e.g., occlusion alert, proximity alert, motion alert) associated with the first object in the first training image; based at least on locating the tag data for the first object, modifying at least a portion of the tag data for the first object from the original simulated data, thereby generating preprocessed training data from the original simulated data; and training a multi-object tracking model with the preprocessed training data to produce a trained multi-object tracker.Type: ApplicationFiled: January 26, 2022Publication date: May 12, 2022Inventors: Ishani CHAKRABORTY, Jonathan C. HANZELKA, Lu YUAN, Pedro Urbina ESCOS, Thomas M. SOEMO
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Publication number: 20220092792Abstract: Training a multi-object tracking model includes: generating a plurality of training images based at least on scene generation information, each training image comprising a plurality of objects to be tracked; generating, for each training image, original simulated data based at least on the scene generation information, the original simulated data comprising tag data for a first object; locating, within the original simulated data, tag data for the first object, based on at least an anomaly alert (e.g., occlusion alert, proximity alert, motion alert) associated with the first object in the first training image; based at least on locating the tag data for the first object, modifying at least a portion of the tag data for the first object from the original simulated data, thereby generating preprocessed training data from the original simulated data; and training a multi-object tracking model with the preprocessed training data to produce a trained multi-object tracker.Type: ApplicationFiled: September 18, 2020Publication date: March 24, 2022Inventors: Ishani CHAKRABORTY, Jonathan C. HANZELKA, Lu YUAN, Pedro Urbina ESCOS, Thomas M. SOEMO
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Publication number: 20210374421Abstract: The disclosure herein enables tracking of multiple objects in a real-time video stream. For each individual frame received from the video stream, a frame type of the frame is determined. Based on the individual frame being an object detection frame type, a set of object proposals is detected in the individual frame, associations between the set of object proposals and a set of object tracks are assigned, and statuses of the set of object tracks are updated based on the assigned associations. Based on the individual frame being an object tracking frame type, single-object tracking is performed on the frame based on each object track of the set of object tracks and the set of object tracks is updated based on the performed single-object tracking. For each frame received, a real-time object location data stream is provided based on the set of object tracks.Type: ApplicationFiled: May 28, 2020Publication date: December 2, 2021Inventors: Ishani CHAKRABORTY, Yi-Ling CHEN, Lu YUAN
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Publication number: 20210035322Abstract: A computing system is configured to train an object classifier. Monocular image data and ground-truth data are received for a scene. Geometric context is determined including a three-dimensional camera position relative to a fixed plane. Regions of interest (RoI) and a set of potential occluders are identified within the image data. For each potential occluder, an occlusion zone is projected onto the fixed plane in three-dimensions. A set of occluded RoIs on the fixed plane are generated for each occlusion zone. Each occluded RoI is projected back to the image data in two-dimensions. The classifier is trained by minimizing a loss function generated by inputting information regarding the RoIs and the occluded RoIs into the classifier, and by minimizing location errors of each RoI and each occluded RoI of the set on the fixed plane based on the ground-truth data. The trained classifier is then output for object detection.Type: ApplicationFiled: October 22, 2020Publication date: February 4, 2021Applicant: Microsoft Technology Licensing, LLCInventors: Ishani CHAKRABORTY, Gang HUA
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Patent number: 10818028Abstract: A computing system is configured to train an object classifier. Monocular image data and ground-truth data are received for a scene. Geometric context is determined including a three-dimensional camera position relative to a fixed plane. Regions of interest (RoI) and a set of potential occluders are identified within the image data. For each potential occluder, an occlusion zone is projected onto the fixed plane in three-dimensions. A set of occluded RoIs on the fixed plane are generated for each occlusion zone. Each occluded RoI is projected back to the image data in two-dimensions. The classifier is trained by minimizing a loss function generated by inputting information regarding the RoIs and the occluded RoIs into the classifier, and by minimizing location errors of each RoI and each occluded RoI of the set on the fixed plane based on the ground-truth data. The trained classifier is then output for object detection.Type: GrantFiled: December 17, 2018Date of Patent: October 27, 2020Assignee: Microsoft Technology Licensing, LLCInventors: Ishani Chakraborty, Gang Hua
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Publication number: 20200193628Abstract: A computing system is configured to train an object classifier. Monocular image data and ground-truth data are received for a scene. Geometric context is determined including a three-dimensional camera position relative to a fixed plane. Regions of interest (RoI) and a set of potential occluders are identified within the image data. For each potential occluder, an occlusion zone is projected onto the fixed plane in three-dimensions. A set of occluded RoIs on the fixed plane are generated for each occlusion zone. Each occluded Rd is projected back to the image data in two-dimensions. The classifier is trained by minimizing a loss function generated by inputting information regarding the RoIs and the occluded RoIs into the classifier, and by minimizing location errors of each Rd and each occluded Rd of the set on the fixed plane based on the ground-truth data. The trained classifier is then output for object detection.Type: ApplicationFiled: December 17, 2018Publication date: June 18, 2020Applicant: Microsoft Technology Licensing, LLCInventors: Ishani CHAKRABORTY, Gang HUA
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Patent number: 10679063Abstract: A computing system for recognizing salient events depicted in a video utilizes learning algorithms to detect audio and visual features of the video. The computing system identifies one or more salient events depicted in the video based on the audio and visual features.Type: GrantFiled: September 4, 2015Date of Patent: June 9, 2020Assignee: SRI InternationalInventors: Hui Cheng, Ajay Divakaran, Elizabeth Shriberg, Harpreet Singh Sawhney, Jingen Liu, Ishani Chakraborty, Omar Javed, David Chisolm, Behjat Siddiquie, Steven S. Weiner
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Patent number: 10121076Abstract: An entity interaction recognition system algorithmically recognizes a variety of different types of entity interactions that may be captured in two-dimensional images. In some embodiments, the system estimates the three-dimensional spatial configuration or arrangement of entities depicted in the image. In some embodiments, the system applies a proxemics-based analysis to determine an interaction type. In some embodiments, the system infers, from a characteristic of an entity detected in an image, an area or entity of interest in the image.Type: GrantFiled: May 2, 2016Date of Patent: November 6, 2018Assignee: SRI InternationalInventors: Ishani Chakraborty, Hui Cheng, Omar Javed
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Publication number: 20180204108Abstract: Automatically training an actor upon the occurrence of a physical condition with respect to that actor. Upon detecting that the actor has the physical condition (e.g., is engaging in or is about to engage in a physical activity), the system determines that training is to be provided for that activity. Upon determining that training is to be provided, the system automatically dispatches training. For instance, the system might cause a human or robot to be dispatched to the actor to show the actor how to perform the activity. Alternatively or instead, a representation of a signal segment may be dispatched to the actor. The representation providing the training to the actor may include a similar target of work to what the actor is presently targeting by the activity. The representation may also include a representation of a person that engaged in the activity properly previously.Type: ApplicationFiled: February 17, 2017Publication date: July 19, 2018Inventors: Vijay Mital, Robin Abraham, Victor Zhu, Liang Du, Ning Zhou, Pramod Kumar Sharma, Ishani Chakraborty
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Publication number: 20160247023Abstract: An entity interaction recognition system algorithmically recognizes a variety of different types of entity interactions that may be captured in two-dimensional images. In some embodiments, the system estimates the three-dimensional spatial configuration or arrangement of entities depicted in the image. In some embodiments, the system applies a proxemics-based analysis to determine an interaction type. In some embodiments, the system infers, from a characteristic of an entity detected in an image, an area or entity of interest in the image.Type: ApplicationFiled: May 2, 2016Publication date: August 25, 2016Inventors: Ishani CHAKRABORTY, Hui CHENG, Omar JAVED
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Patent number: 9330296Abstract: An entity interaction recognition system algorithmically recognizes a variety of different types of entity interactions that may be captured in two-dimensional images. In some embodiments, the system estimates the three-dimensional spatial configuration or arrangement of entities depicted in the image. In some embodiments, the system applies a proxemics-based analysis to determine an interaction type. In some embodiments, the system infers, from a characteristic of an entity detected in an image, an area or entity of interest in the image.Type: GrantFiled: September 9, 2013Date of Patent: May 3, 2016Assignee: SRI INTERNATIONALInventors: Ishani Chakraborty, Hui Cheng, Omar Javed
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Patent number: 9268994Abstract: A unified framework detects and classifies people interactions in unconstrained user generated images. Previous approaches directly map people/face locations in two-dimensional image space into features for classification. Among other things, the disclosed framework estimates a camera viewpoint and people positions in 3D space and then extracts spatial configuration features from explicit three-dimensional people positions.Type: GrantFiled: August 15, 2013Date of Patent: February 23, 2016Assignee: SRI INTERNATIONALInventors: Ishani Chakraborty, Hui Cheng, Omar Javed
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Publication number: 20160004911Abstract: A computing system for recognizing salient events depicted in a video utilizes learning algorithms to detect audio and visual features of the video. The computing system identifies one or more salient events depicted in the video based on the audio and visual features.Type: ApplicationFiled: September 4, 2015Publication date: January 7, 2016Inventors: Hui Cheng, Ajay Divakaran, Elizabeth Shriberg, Harpreet Singh Sawhney, Jingen Liu, Ishani Chakraborty, Omar Javed, David Chisolm, Behjat Siddiquie, Steven S. Weiner