Object detection and identification
US-9275302-B1 · Mar 1, 2016 · US
US12229692B2 · US · B2
| Field | Value |
|---|---|
| Publication number | US-12229692-B2 |
| Application number | US-202318234255-A |
| Country | US |
| Kind code | B2 |
| Filing date | Aug 15, 2023 |
| Priority date | Jan 31, 2019 |
| Publication date | Feb 18, 2025 |
| Grant date | Feb 18, 2025 |
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Systems and methods are provided for intelligently monitoring environments, classifying objects within such environments, detecting events within such environments, receiving and propagating input concerning image information from multiple users in a collaborative environment, identifying and responding to situational abnormalities or situations of interest based on such detections and/or user inputs.
Opening claim text (preview).
The invention claimed is: 1. A system for intelligently monitoring an environment, comprising: one or more processors; and a memory storing instructions that, when executed by the one or more processors, cause the system to: obtain content representing an environment, the content comprising a plurality of frames, wherein the content comprises video content including an object captured from two angles; identify, based on the content, one or more discrete objects observed within the environment; track the object of the one or more discrete objects across the frames; detect an event that deviates from one or more patterns; in response to determining that the event deviates from the one or more patterns, flag the event; generate a three-dimensional (3D) model from the two angles of the object according to a 3D reconstruction algorithm; and augment a map of the environment with the generated 3D model. 2. The system of claim 1 , wherein the instructions further cause the system to: receive an indication of a missed detection of an instance of the object or a different object of the discrete objects; receive a label of an object type corresponding to the missed detection; and learn one or more attributes of the missed detection to recognize any future instances of an object type. 3. The system of claim 1 , wherein the instructions further cause the system to: learn one or more patterns associated with the detected event, wherein the one or more patterns comprise seasonal changes. 4. The system of claim 3 , wherein the learning the one or more patterns associated with the detected event comprises learning a range associated with the detected event; and the determining that the detected event deviates from the learned one or more patterns comprises determining that the detected event is outside of or fails to satisfy a corresponding range. 5. The system of claim 1 , wherein the memory stored instructions that, when executed by the one or more processors, further causes the system to: present the map of the environment; and simultaneously present a playback of the tracked object in a separate pane. 6. The system of claim 5 , wherein the memory stored instructions that, when executed by the one or more processors, further causes the system to: identify a geographic boundary associated with the playback of the tracked object; determine a corresponding boundary in the map; detect an input to zoom in the geographic boundary; and change the corresponding boundary in the map based on the input to zoom in. 7. The system of claim 1 , wherein the memory stored instructions that, when executed by the one or more processors, further causes the system to: detect a number of occupants within the object based on a heat signature from thermal imagery; and detect one or more attributes about an occupant of the occupants. 8. The system of claim 1 , wherein the detecting one or more events associated with the tracked object comprises: detecting changes in the environment; identifying candidate events based on the detected changes; and comparing the candidate events with templates while accounting for a scaling, angle, or orientation difference between the object and corresponding objects in the templates. 9. The system of claim 1 , wherein the instructions further cause the system to: determine a view field of a sensor capturing the content. 10. The system of claim 1 , wherein the instructions further cause the system to: in response to detecting the one or more events, present a snapshot of a particular frame corresponding to the one or more detected events. 11. A method being implemented by a computing system including one or more physical processors and storage media storing machine-readable instructions, the method comprising: obtaining content representing an environment, the content comprising a plurality of frames, wherein the content comprises video content including an object captured from two angles; identifying, based on the content, one or more discrete objects observed within the environment; tracking the object of the one or more discrete objects across the frames; detecting an event that deviates from one or more patterns; in response to determining that the event deviates from the one or more patterns, flag the event; generating a three-dimensional (3D) model from the two angles of the object according to a 3D reconstruction algorithm; and augmenting a map of the environment with the generated 3D model. 12. The method of claim 11 , further comprising: receiving an indication of a missed detection of an instance of the object or a different object of the discrete objects; receiving a label of an object type corresponding to the missed detection; and learning one or more attributes of the missed detection to recognize any future instances of an object type. 13. The method of claim 11 , further comprising: learning one or more patterns associated with the detected event, wherein the one or more patterns comprise seasonal changes. 14. The method of claim 13 , wherein the learning the one or more patterns associated with the detected event comprises learning a range associated with the detected event; and the determining that the detected event deviates from the learned one or more patterns comprises determining that the detected event is outside of or fails to satisfy a corresponding range. 15. The method of claim 11 , further comprising: presenting the map of the environment; and simultaneously presenting a playback of the tracked object in a separate pane. 16. The method of claim 11 , further comprising: identifying a geographic boundary associated with the playback of the tracked object; determining a corresponding boundary in the map; detecting an input to zoom in the geographic boundary; and changing the corresponding boundary in the map based on the input to zoom in. 17. The method of claim 11 , further comprising: detecting a number of occupants within the object based on a heat signature from thermal imagery; and detecting one or more attributes about an occupant of the occupants. 18. The method of claim 11 , wherein the detecting one or more events associated with the tracked object comprises: detecting changes in the environment; identifying candidate events based on the detected changes; and comparing the candidate events with templates while accounting for a scaling, angle, or orientation difference between the object and corresponding objects in the templates. 19. The method of claim 11 , further comprising: determining a view field of a sensor capturing the content. 20. The method of claim 11 , further comprising: in response to detecting the one or more events, presenting a snapshot of a particular frame corresponding to the one or more detected events.
Surveillance or monitoring of activities, e.g. for recognising suspicious objects (recognising microscopic objects G06V20/69) · CPC title
Classification techniques · CPC title
Three-dimensional [3D] objects · CPC title
Analysis of motion (motion estimation for coding, decoding, compressing or decompressing digital video signals H04N19/43, H04N19/51) · CPC title
Selection of displayed objects or displayed text elements (G06F3/0482 takes precedence) · CPC title
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