WO2012148258A1 - Abrupt object movement detection system and method thereof - Google Patents
Abrupt object movement detection system and method thereof Download PDFInfo
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- WO2012148258A1 WO2012148258A1 PCT/MY2012/000094 MY2012000094W WO2012148258A1 WO 2012148258 A1 WO2012148258 A1 WO 2012148258A1 MY 2012000094 W MY2012000094 W MY 2012000094W WO 2012148258 A1 WO2012148258 A1 WO 2012148258A1
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- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/20—Analysis of motion
- G06T7/254—Analysis of motion involving subtraction of images
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- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10016—Video; Image sequence
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30196—Human being; Person
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- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30232—Surveillance
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- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30236—Traffic on road, railway or crossing
Definitions
- the present invention relates generally to a detection system, more particularly to a detection system for detecting abrupt object movement in inconsistent frame rate and the method thereof .
- Video surveillance systems are widely used apparatuses to detect and monitor objects within a monitoring area. Usually those video surveillance systems are used to detect and track individuals or vehicles entering or leaving a building facility or to monitor individuals within the monitoring area where the safety of the occupants may be of concern. And another example of application for the surveillance system is to monitor crowds as surveillance cameras are low cost and require no physical contact with the object being monitored.
- the inconsistent frame rate includes low frame rate that resulting in lagging issue. Therefore when the video input containing lagging problem is converted to image frame, there will be some missing frame of images due to lagging video as shown in Figures la and lb. This situation gives large error because the movement of moving object is measured by measuring the different locations of the moving object between every two frame of images. The higher of the differences will be interpreted as higher movement. Therefore, if there is some missing frame of images, the different of location of moving object between two frames will be higher. Then the system will interpret it as high movement even though there is no high movement in the sample data as shown in Figure lc. Hence, false positive happened which is when no any abnormalities in the monitoring area but the system triggered the detection.
- the present invention aims to provide a system and method to detect abrupt object movement with the ability to work in unstable network is needed.
- an abrupt object movement detection system comprises at least one capturing device for capturing a sequence of image frames at a monitoring area, means for registering frame of images having different types of frame rates, wherein motion blobs of the images are extracted to obtain object's displacement information for categorizing into predefined categories of frame rates and the maximum displacement information for each frame rate is chosen as a filtering threshold for the respective frame rate categories; and means for detecting abrupt object movement by filtering a scene displacement information with the filtering threshold based on a corresponding predefined frame rate category and determining abrupt movement from total density of the filtered displacement information.
- a method for detecting abrupt object movement with a detection system comprising the steps of capturing a sequence of image frames at a monitoring area as input images for processing, registering frame of images having different types of frame rates by extracting motion blobs of the images to obtain object's displacement information for categorizing into predefined categories of frame rates and determining the maximum displacement information as a filtering threshold for the respective frame rate categories, and detecting an abrupt object movement event by filtering a scene displacement information with the filtering threshold based on a corresponding predefined frame rate category and determining abrupt movement from total density of the filtered displacement information.
- Figure la shows a series of frame of image converted from stream of video for a similar scene at different frame rate of an existing detection system
- Figure lb shows a sequence of frame of image having some of the frames of image missing due to unstable network issuer-
- Figure lc illustrates two frames of images which are interpreted as high movement due to the issue shown in figure lb;
- Figure 2 is a flowchart showing the method for the registration process of the abrupt object movement detection system of the present invention
- Figure 3a depicts an original input image
- Figure 3b shows the image of figure 3a with extracted motion blobs
- Figure 3c is the image of figure 3b showing the displacement information
- Figure 4 is a schematic diagram showing the step of buffering the displacement information and examining the information to determine the frame rate category;
- Figure 5 is a flowchart showing the method for the step of detecting process of the of the abrupt object movement detection system of the present invention
- Figure 6 shows a schematic diagram showing the displacement information within a sliding window filtered based on the selected filtering threshold and the remaining displacement information
- Figure 7 illustrates an example of connected movement information .
- the abrupt object movement detection system (10) includes at least one capturing device (11) for capturing a sequence of image frames at a monitoring area, means for registering (12) frame of images having different types of frame rates for acquiring optimal filtering threshold, means for detecting (13) abrupt object movement by filtering the displacement information with the filtering threshold based on the frame rate and determining from the total density of the filtered movements, and a displaying device (40) for alerting the user if the abrupt object movement event is detected.
- frame rates There are three different types of frame rates which include the low, medium and high frame rate.
- the means for registering (12) performs the registration process as shown in Figure 2 and the means for detecting (13) performs the online detection process as shown in Figure 4.
- the system (10) will receive a sequence of image frames which is captured by the capturing device (11). These image frames will then be processed (14) by extracting all moving pixels in the current frame and in this case, current frame is compared to the background image for detecting motion blobs (15).
- the moving pixels are called motion pixels and they represent the moving object of interest within the current frame. Group of connected motion pixels is called motion blobs.
- Figure 3a shows an original input image, follows by the example of extracted motion blobs (21) shown in Figure 3b and the displacement information which is the optical flow vector shown in Figure 3c.
- the extracted motion blobs With the extracted motion blobs, the current motion blob of the same object and its previous motion blob in previous frame are compared and the optical flow is computed to extract the displacement information (16) .
- the extracted motion blobs are used as the mask in computing the optical flow, whereby the stray motion vector resultant from the background pixels will be ignored.
- each object's displacement information is computed, the cumulative or average of the data within current frame is then computed and the value is stored into a buffer.
- the registration process followed by the step of buffering the displacement information (17) and examining in a sliding window to classify (18) them into three different types of frame rates namely the low, medium and high frame rate as shown in Figure .
- each sliding window it contains the same number of image frame but the time duration for each window is different and it is depending on the network.
- the frame rate for each sliding window is determined by dividing the number of frames to time duration in second of the window. Then the sliding window is classified into low frame rate category if the computed frame rate is within 0 to 6 fps (frame per second), or into medium frame rate category if the computed frame rate is within 7 to 12 fps or high frame rate category if the computed frame rate within 13 to 25 fps.
- the maximum displacement data in each sliding window is extracted (19) .
- all maximum displacement data for each frame rate categories is compared (20) and the maximum value is chosen as the filtering threshold for that frame rate category. Therefore, three optimal filtering thresholds for three different frame rate categories are obtained .
- the initial processes are the same as in the registration process.
- the moving objects are extracted from the scene (31) which is the same as in step (15) in the registration process.
- the displacement information of each object in the scene is extracted (32).
- the scene displacement information which is the average displacement information in current frame is computed.
- the scene displacement information is stored into a buffer.
- the system (10) then performs the step of buffering the displacement (33) wherein if the buffer size is more than the minimum window size, the window is examined to determine its frame rate category (34).
- the filtering threshold is selected accordingly.
- the displacement information within the sliding window is filtered based on the selected filtering threshold. If it is lower than the selected filtering threshold, the displacement information will be removed (35) as shown in Figure 6.
- the detection process is followed by grouping the remaining displacement information (36) . In this step, every single magnitude will be linking to each other with their connected neighbor and the example of connected displacement information is shown in Figure 7.
- the total density of each group of displacement information is then calculated (37) .
- the total density of each group is then compared to the sensitivity threshold to determine (38) the event of an abrupt object movement. If an event is detected, the system will alert the user by displaying the information on the displaying device (40) .
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Abstract
An abrupt object movement detection system (10) comprises at least one capturing device (11) for capturing a sequence of image frames at a monitoring area, means for registering (12) frame of images having different types of frame rates, wherein motion blobs of the images are extracted to obtain object's displacement information for categorizing into predefined categories of frame rates and the maximum displacement information for each frame rate is chosen as a filtering threshold for the respective frame rate categories; and means for detecting (13) abrupt object movement by filtering a scene displacement information with the filtering threshold based on a corresponding predefined frame rate category and determining abrupt movement from total density of the filtered displacement information.
Description
Abrupt Object Movement Detection System and Method Thereof
Field of Invention
The present invention relates generally to a detection system, more particularly to a detection system for detecting abrupt object movement in inconsistent frame rate and the method thereof .
Background of the Invention
Video surveillance systems are widely used apparatuses to detect and monitor objects within a monitoring area. Usually those video surveillance systems are used to detect and track individuals or vehicles entering or leaving a building facility or to monitor individuals within the monitoring area where the safety of the occupants may be of concern. And another example of application for the surveillance system is to monitor crowds as surveillance cameras are low cost and require no physical contact with the object being monitored.
Abrupt movement indicates object moves in large displacement in a short time. This detection is useful to determine abnormal crowd activities including riot and panic incident due to danger. However, accurate crowd monitoring is a challenging problem because the environments and groups of
people vary and unpredictable and also due to the inconsistent frame rate of the monitoring system. The accuracy of the detection is dropped due to the high false positive in the system and the false positive is the situation when the system gives the alert of the detection during no actual event. And this major cause of false positive is due to the unstable networks that caused inconsistent frame rate.
The inconsistent frame rate includes low frame rate that resulting in lagging issue. Therefore when the video input containing lagging problem is converted to image frame, there will be some missing frame of images due to lagging video as shown in Figures la and lb. This situation gives large error because the movement of moving object is measured by measuring the different locations of the moving object between every two frame of images. The higher of the differences will be interpreted as higher movement. Therefore, if there is some missing frame of images, the different of location of moving object between two frames will be higher. Then the system will interpret it as high movement even though there is no high movement in the sample data as shown in Figure lc. Hence, false positive happened which is when no any abnormalities in the monitoring area but the system triggered the detection.
A system that detects abrupt object movement in inconsistent frame rate has not been developed. Therefore, the present
invention aims to provide a system and method to detect abrupt object movement with the ability to work in unstable network is needed.
Other objects of this invention will become apparent on the reading of this entire disclosure.
Summary of -the Invention
In the present invention, an abrupt object movement detection system comprises at least one capturing device for capturing a sequence of image frames at a monitoring area, means for registering frame of images having different types of frame rates, wherein motion blobs of the images are extracted to obtain object's displacement information for categorizing into predefined categories of frame rates and the maximum displacement information for each frame rate is chosen as a filtering threshold for the respective frame rate categories; and means for detecting abrupt object movement by filtering a scene displacement information with the filtering threshold based on a corresponding predefined frame rate category and determining abrupt movement from total density of the filtered displacement information.
A method for detecting abrupt object movement with a detection system, the method comprising the steps of capturing a
sequence of image frames at a monitoring area as input images for processing, registering frame of images having different types of frame rates by extracting motion blobs of the images to obtain object's displacement information for categorizing into predefined categories of frame rates and determining the maximum displacement information as a filtering threshold for the respective frame rate categories, and detecting an abrupt object movement event by filtering a scene displacement information with the filtering threshold based on a corresponding predefined frame rate category and determining abrupt movement from total density of the filtered displacement information.
Brief Description of the Drawings
Other objects, features, and advantages of the invention will be apparent from the following description when read with o
reference to the accompanying drawings. In the drawings, wherein like reference numerals denote corresponding parts throughout the several views:
Figure la shows a series of frame of image converted from stream of video for a similar scene at different frame rate of an existing detection system;
Figure lb shows a sequence of frame of image having some of the frames of image missing due to unstable network issuer- Figure lc illustrates two frames of images which are interpreted as high movement due to the issue shown in figure lb;
Figure 2 is a flowchart showing the method for the registration process of the abrupt object movement detection system of the present invention;
Figure 3a depicts an original input image;
Figure 3b shows the image of figure 3a with extracted motion blobs;
Figure 3c is the image of figure 3b showing the displacement information; Figure 4 is a schematic diagram showing the step of buffering the displacement information and examining the information to determine the frame rate category;
Figure 5 is a flowchart showing the method for the step of detecting process of the of the abrupt object movement detection system of the present invention;
Figure 6 shows a schematic diagram showing the displacement information within a sliding window filtered based on the selected filtering threshold and the remaining displacement information; and
Figure 7 illustrates an example of connected movement information .
Detailed Description of the Preferred Embodiments
In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the invention. However, it will be understood by those of ordinary skill in the art that the invention may be practiced without these specific details. In other instances, well-known methods, procedures and/or components have not been described in detail so as not to obscure the invention. Reference will now be made in detail to the preferred embodiments of the present invention, examples of which are illustrated in the accompanying drawings.
An abrupt object movement detection system (10) related to the invention will be described with reference to the accompanying drawings. The abrupt object movement detection system (10) includes at least one capturing device (11) for capturing a sequence of image frames at a monitoring area, means for
registering (12) frame of images having different types of frame rates for acquiring optimal filtering threshold, means for detecting (13) abrupt object movement by filtering the displacement information with the filtering threshold based on the frame rate and determining from the total density of the filtered movements, and a displaying device (40) for alerting the user if the abrupt object movement event is detected. There are three different types of frame rates which include the low, medium and high frame rate. The means for registering (12) performs the registration process as shown in Figure 2 and the means for detecting (13) performs the online detection process as shown in Figure 4.
In the registration process, the system (10) will receive a sequence of image frames which is captured by the capturing device (11). These image frames will then be processed (14) by extracting all moving pixels in the current frame and in this case, current frame is compared to the background image for detecting motion blobs (15). The moving pixels are called motion pixels and they represent the moving object of interest within the current frame. Group of connected motion pixels is called motion blobs.
Figure 3a shows an original input image, follows by the example of extracted motion blobs (21) shown in Figure 3b and the displacement information which is the optical flow vector
shown in Figure 3c. With the extracted motion blobs, the current motion blob of the same object and its previous motion blob in previous frame are compared and the optical flow is computed to extract the displacement information (16) . In this stage, the extracted motion blobs are used as the mask in computing the optical flow, whereby the stray motion vector resultant from the background pixels will be ignored.
After each object's displacement information is computed, the cumulative or average of the data within current frame is then computed and the value is stored into a buffer. The registration process followed by the step of buffering the displacement information (17) and examining in a sliding window to classify (18) them into three different types of frame rates namely the low, medium and high frame rate as shown in Figure . In each sliding window, it contains the same number of image frame but the time duration for each window is different and it is depending on the network.
The frame rate for each sliding window is determined by dividing the number of frames to time duration in second of the window. Then the sliding window is classified into low frame rate category if the computed frame rate is within 0 to 6 fps (frame per second), or into medium frame rate category if the computed frame rate is within 7 to 12 fps or high frame rate category if the computed frame rate within 13 to 25 fps.
Once each sliding window is categorized into its frame rate categories, then the maximum displacement data in each sliding window is extracted (19) . Lastly, all maximum displacement data for each frame rate categories is compared (20) and the maximum value is chosen as the filtering threshold for that frame rate category. Therefore, three optimal filtering thresholds for three different frame rate categories are obtained .
Then, in the detection process as shown in Figure 5, the initial processes are the same as in the registration process. For each incoming image frame, the moving objects are extracted from the scene (31) which is the same as in step (15) in the registration process. Then, by comparing the current motion blobs with their' corresponding motion blobs in previous frame, the displacement information of each object in the scene is extracted (32). After obtaining each object's displacement information, then the scene displacement information which is the average displacement information in current frame is computed. The scene displacement information is stored into a buffer. The system (10) then performs the step of buffering the displacement (33) wherein if the buffer size is more than the minimum window size, the window is examined to determine its frame rate category (34). Dependent on which frame rate category the window is belong to, the filtering threshold is selected accordingly.
The displacement information within the sliding window is filtered based on the selected filtering threshold. If it is lower than the selected filtering threshold, the displacement information will be removed (35) as shown in Figure 6. The detection process is followed by grouping the remaining displacement information (36) . In this step, every single magnitude will be linking to each other with their connected neighbor and the example of connected displacement information is shown in Figure 7. Lastly, the total density of each group of displacement information is then calculated (37) . The total density of each group is then compared to the sensitivity threshold to determine (38) the event of an abrupt object movement. If an event is detected, the system will alert the user by displaying the information on the displaying device (40) .
As will be readily apparent to those skilled in the art, the present invention may easily be produced in other specific forms without departing from its essential characteristics. The present embodiments is, therefore, to be considered as merely illustrative and not restrictive, the scope of the invention being indicated by the claims rather than the foregoing description, and all changes which come within therefore intended to be embraced therein.
Claims
1. An abrupt object movement detection system (10) comprising: at least one capturing device (11) for capturing a sequence of image frames at a monitoring area as input images for processing;
means for registering (12) frame of images having different types of frame rates, wherein motion blobs of said images are extracted to obtain object's displacement information for categorizing into predefined categories of frame rates and the maximum displacement information for each frame rate is chosen as a filtering threshold for the respective frame rate categories; and
means for detecting (13) abrupt object movement by filtering a scene displacement information with the filtering threshold based on a corresponding predefined frame rate category and determining abrupt movement from total density of the filtered displacement information.
2. The abrupt object movement detection system (10) as claimed in claim 1, wherein said predefined categories of frame rates include a low frame rate, a medium frame rate and a high frame rate .
3. The abrupt object movement detection system (10) as claimed in claim 2, wherein said low frame rate category is having the frame rate approximately within 0 to 6 frame per second, fps .
4. The abrupt object movement detection system (10) as claimed in claim 3, wherein said medium frame rate is having the frame rate approximately within 7 to 12 frames per second, fps .
5. The abrupt object movement detection system (10) as claimed in claim 2, wherein said high frame rate is having the frame rate approximately within 13 to 25 frames per second, fps .
6. The abrupt object movement detection system (10) as claimed in claim 1, wherein said system further comprising a displaying device (40) for alerting the user if an abrupt object movement event is detected
7. A method for detecting abrupt object movement with a detection system (10), said method comprising the steps of: capturing a■ sequence of image frames (11) at a monitoring area as input images for processing (14);
registering frame of images having different types of frame rates by extracting motion blobs (15) of said images to obtain object's displacement information (16) for categorizing (18) into predefined categories of frame rates and determining the maximum displacement information (19) as a filtering threshold for the respective frame rate categories; and
detecting an abrupt object movement event by filtering a scene displacement information (35) with said filtering threshold based on a corresponding predefined frame rate category and determining (38) abrupt movement from total density of said filtered displacement information.
8. The method for detecting abrupt object movement with a detection system (10) as claimed in claim 7, wherein said step of extracting motion blobs (15) of said images to obtain object's displacement information (16) includes the step of comparing the current motion blobs with the corresponding motion blobs in previous frame to compute the optical flow for extracting the object's displacement information.
9. The method for detecting abrupt object movement with a detection system (10) as claimed in claim 7, wherein said step of categorizing (18) into predefined categories of frame rates includes the step of examining in a sliding window to determine the frame rate of the image.
10. The method for detecting abrupt object movement with detection system (10) as claimed in claim 9, wherein sai predefined categories of frame rates include a low frame rate a medium frame rate and a high frame rate.
11. The method for detecting abrupt object movement with a detection system (10) as claimed in claim 7, wherein said corresponding predefined frame rate category is determined by the steps of extracting motion blobs (31) from the scene to obtain object's displacement information (32), computing the average displacement information in current frame of each object in the scene and buffering said displacement information (33) to determine its frame rate category (34).
12. The method for detecting abrupt object movement with a detection system (10) as claimed in claim 7, wherein said prior to the step of determining (38) includes the steps grouping the remaining displacement information (36) and calculating total density of the groups of displacement information (37).
13. The method for detecting abrupt object movement with a detection system (10) as claimed in claim 12, wherein said step of determining (38) includes the step of comparing the total density of each group with the sensitivity threshold.
14. The method for detecting abrupt object movement with a detection system (10) as claimed in claim 7, wherein said method further comprising the step of alerting the user by displaying information at a displaying device (40) if an abrupt object movement event is detected.
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| MYPI2011001929 | 2011-04-29 | ||
| MYPI2011001929A MY178641A (en) | 2011-04-29 | 2011-04-29 | Abrupt object movement detection system and method thereof |
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| Publication Number | Publication Date |
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| WO2012148258A1 true WO2012148258A1 (en) | 2012-11-01 |
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| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| PCT/MY2012/000094 Ceased WO2012148258A1 (en) | 2011-04-29 | 2012-04-27 | Abrupt object movement detection system and method thereof |
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| MY (1) | MY178641A (en) |
| WO (1) | WO2012148258A1 (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105308876A (en) * | 2012-11-29 | 2016-02-03 | 康宁光电通信有限责任公司 | Hybrid intra-cell / inter-cell remote unit antenna bonding in multiple-input, multiple-output (MIMO) distributed antenna systems (DAS) |
| US10055669B2 (en) | 2016-08-12 | 2018-08-21 | Qualcomm Incorporated | Methods and systems of determining a minimum blob size in video analytics |
| US10169661B2 (en) | 2014-03-28 | 2019-01-01 | International Business Machines Corporation | Filtering methods for visual object detection |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5311306A (en) * | 1991-07-19 | 1994-05-10 | Kabushiki Kaisha Toshiba | Motion detecting circuit for video signal processor |
| US20070104462A1 (en) * | 2005-11-10 | 2007-05-10 | Sony Corporation | Image signal processing device, imaging device, and image signal processing method |
| US20100265344A1 (en) * | 2009-04-15 | 2010-10-21 | Qualcomm Incorporated | Auto-triggered fast frame rate digital video recording |
-
2011
- 2011-04-29 MY MYPI2011001929A patent/MY178641A/en unknown
-
2012
- 2012-04-27 WO PCT/MY2012/000094 patent/WO2012148258A1/en not_active Ceased
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5311306A (en) * | 1991-07-19 | 1994-05-10 | Kabushiki Kaisha Toshiba | Motion detecting circuit for video signal processor |
| US20070104462A1 (en) * | 2005-11-10 | 2007-05-10 | Sony Corporation | Image signal processing device, imaging device, and image signal processing method |
| US20100265344A1 (en) * | 2009-04-15 | 2010-10-21 | Qualcomm Incorporated | Auto-triggered fast frame rate digital video recording |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105308876A (en) * | 2012-11-29 | 2016-02-03 | 康宁光电通信有限责任公司 | Hybrid intra-cell / inter-cell remote unit antenna bonding in multiple-input, multiple-output (MIMO) distributed antenna systems (DAS) |
| CN105308876B (en) * | 2012-11-29 | 2018-06-22 | 康宁光电通信有限责任公司 | Remote unit antennas in distributing antenna system combines |
| US10169661B2 (en) | 2014-03-28 | 2019-01-01 | International Business Machines Corporation | Filtering methods for visual object detection |
| US10055669B2 (en) | 2016-08-12 | 2018-08-21 | Qualcomm Incorporated | Methods and systems of determining a minimum blob size in video analytics |
Also Published As
| Publication number | Publication date |
|---|---|
| MY178641A (en) | 2020-10-19 |
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