WO2019152177A3 - System and method for neuromorphic visual activity classification based on foveated detection and contextual filtering - Google Patents
System and method for neuromorphic visual activity classification based on foveated detection and contextual filtering Download PDFInfo
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- WO2019152177A3 WO2019152177A3 PCT/US2019/013513 US2019013513W WO2019152177A3 WO 2019152177 A3 WO2019152177 A3 WO 2019152177A3 US 2019013513 W US2019013513 W US 2019013513W WO 2019152177 A3 WO2019152177 A3 WO 2019152177A3
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- foveated
- activity
- detection
- classification
- activity classification
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
- G06N20/10—Machine learning using kernel methods, e.g. support vector machines [SVM]
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/044—Recurrent networks, e.g. Hopfield networks
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/255—Detecting or recognising potential candidate objects based on visual cues, e.g. shapes
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V2201/00—Indexing scheme relating to image or video recognition or understanding
- G06V2201/08—Detecting or categorising vehicles
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/20—Movements or behaviour, e.g. gesture recognition
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- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Software Systems (AREA)
- Artificial Intelligence (AREA)
- Computing Systems (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Mathematical Physics (AREA)
- General Engineering & Computer Science (AREA)
- Computational Linguistics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Multimedia (AREA)
- Medical Informatics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Image Analysis (AREA)
Abstract
Described is a system for visual activity recognition. In operation, the system detects a set of objects of interest (OI) in video data and determines an object classification for each object in the set of OI, the set including at least one OI. A corresponding activity track is formed for each object in the set of OI by tracking each object across frames. Using a feature extractor, the system determines a corresponding feature in the video data for each OI, which is then used to determine a corresponding initial activity classification for each OI. One or more OI are then detected in each activity track via foveation, with the initial object detection and foveated object detection thereafter being appended into a new detected-objects list. Finally, a final classification is provided for each activity track using the new detected-objects list and filtering the initial activity classification results using contextual logic.
Priority Applications (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
EP19748018.9A EP3746938A4 (en) | 2018-01-30 | 2019-01-14 | System and method for neuromorphic visual activity classification based on foveated detection and contextual filtering |
CN201980006835.9A CN111566661B (en) | 2018-01-30 | 2019-01-14 | Systems, methods, computer-readable media for visual activity classification |
Applications Claiming Priority (6)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US15/883,822 | 2018-01-30 | ||
US15/883,822 US11055872B1 (en) | 2017-03-30 | 2018-01-30 | Real-time object recognition using cascaded features, deep learning and multi-target tracking |
US201862642959P | 2018-03-14 | 2018-03-14 | |
US62/642,959 | 2018-03-14 | ||
US15/947,032 US10997421B2 (en) | 2017-03-30 | 2018-04-06 | Neuromorphic system for real-time visual activity recognition |
US15/947,032 | 2018-04-06 |
Publications (2)
Publication Number | Publication Date |
---|---|
WO2019152177A2 WO2019152177A2 (en) | 2019-08-08 |
WO2019152177A3 true WO2019152177A3 (en) | 2019-10-10 |
Family
ID=67479383
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
PCT/US2019/013513 WO2019152177A2 (en) | 2018-01-30 | 2019-01-14 | System and method for neuromorphic visual activity classification based on foveated detection and contextual filtering |
Country Status (3)
Country | Link |
---|---|
EP (1) | EP3746938A4 (en) |
CN (1) | CN111566661B (en) |
WO (1) | WO2019152177A2 (en) |
Families Citing this family (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN111652128B (en) * | 2020-06-02 | 2023-09-01 | 浙江大华技术股份有限公司 | High-altitude power operation safety monitoring method, system and storage device |
US20230206615A1 (en) * | 2021-12-29 | 2023-06-29 | Halliburton Energy Services, Inc. | Systems and methods to determine an activity associated with an object of interest |
US11776247B2 (en) | 2022-01-07 | 2023-10-03 | Tomahawk Robotics | Classification parallelization architecture |
KR20230134846A (en) * | 2022-03-15 | 2023-09-22 | 연세대학교 산학협력단 | Multiscale object detection device and method |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
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US20110081043A1 (en) * | 2009-10-07 | 2011-04-07 | Sabol Bruce M | Using video-based imagery for automated detection, tracking, and counting of moving objects, in particular those objects having image characteristics similar to background |
US20140218468A1 (en) * | 2012-04-05 | 2014-08-07 | Augmented Vision Inc. | Wide-field of view (fov) imaging devices with active foveation capability |
US9230302B1 (en) * | 2013-03-13 | 2016-01-05 | Hrl Laboratories, Llc | Foveated compressive sensing system |
US20170132468A1 (en) * | 2015-11-06 | 2017-05-11 | The Boeing Company | Systems and methods for object tracking and classification |
Family Cites Families (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
BRPI0806968B1 (en) * | 2007-02-08 | 2018-09-18 | Behavioral Recognition Sys Inc | method for processing video frame stream and associated system |
US9008366B1 (en) | 2012-01-23 | 2015-04-14 | Hrl Laboratories, Llc | Bio-inspired method of ground object cueing in airborne motion imagery |
US9147255B1 (en) | 2013-03-14 | 2015-09-29 | Hrl Laboratories, Llc | Rapid object detection by combining structural information from image segmentation with bio-inspired attentional mechanisms |
-
2019
- 2019-01-14 WO PCT/US2019/013513 patent/WO2019152177A2/en unknown
- 2019-01-14 CN CN201980006835.9A patent/CN111566661B/en active Active
- 2019-01-14 EP EP19748018.9A patent/EP3746938A4/en active Pending
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20110081043A1 (en) * | 2009-10-07 | 2011-04-07 | Sabol Bruce M | Using video-based imagery for automated detection, tracking, and counting of moving objects, in particular those objects having image characteristics similar to background |
US20140218468A1 (en) * | 2012-04-05 | 2014-08-07 | Augmented Vision Inc. | Wide-field of view (fov) imaging devices with active foveation capability |
US9230302B1 (en) * | 2013-03-13 | 2016-01-05 | Hrl Laboratories, Llc | Foveated compressive sensing system |
US20170132468A1 (en) * | 2015-11-06 | 2017-05-11 | The Boeing Company | Systems and methods for object tracking and classification |
Non-Patent Citations (1)
Title |
---|
JEFF DONAHUE ET AL.: "Long-term Recurrent Convolutional Networks for Visual Recognition and Description", THE IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR, 2015, pages 2625 - 2634, XP032793708, DOI: 10.1109/CVPR.2015.7298878 * |
Also Published As
Publication number | Publication date |
---|---|
WO2019152177A2 (en) | 2019-08-08 |
CN111566661B (en) | 2023-11-17 |
EP3746938A2 (en) | 2020-12-09 |
CN111566661A (en) | 2020-08-21 |
EP3746938A4 (en) | 2021-10-06 |
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