How Do Video Analytics Work?
Basic analytics use rule-based algorithms that follow a decision tree of “if/then” questions to predict whether an object in a video could be a possible threat, after a process of elimination, if the software has decided the incident is a threat, it generates alerts and/or triggers recording of the event.
Video analytic software isn’t watching a continuous stream of video. Instead, it is isolating and analyzing individual frames in sequence. Depending on the algorithm, the software will ask multiple questions of each image and applying the if/then rules until it is able to determine whether the condition is a true alarm or not.
Central Processing vs. Edge Processing
Video analytic software can be run centrally on servers on premise which is known as central processing or embedded into the camera itself which is known as edge processing.
Recent advances with camera SOC (System on a Chip) such as Ambarella, have increased processing power, allowing higher accuracy analytics possible directly on the camera.
By using video analytics on the edge, the video is processed and analyzed prior to streaming to recording server (either on premise or in the cloud) resulting in increased bandwidth and storage savings.
Edge Recording
Given the rise of analytics and increased storage directly on the camera, edge recording has become a reality. Now using cameras such as Digital Watchdog’s CAAS (Camera as a System) camera’s, a year of video can now be easily stored directly on the camera eliminating the need for a central recording server. In addition, archives could be permanently stored in the cloud for a completely redundant, economical solution.
Common Types of Video Analytics
Motion Detection: Video motion detection (VMD) is the most basic video analytic. It looks for pixel changes within the video to determine if motion is present.
Line Crossing: Detect people of objects crossing the detection line.
Counting Line: Calculate the number of people or objects crossing the detection line.
Loitering: Detect people or objects entering the detection zone but not exiting after a set duration.
Object Removal: Detect in real-time objects removed from the detection zone.
LPR (License Plate Recognition): Detect and record license plate numbers
Facial Recognition: Detect and recognize faces
Conclusion
Utilizing video analytics can provide significant savings in time and money. This sub-segment of the industry will be growing exponentially over the next 5 years and will be a significant game changer for the security industry.
The use of video analytics will be ubiquitous in our daily lives and invaluable in helping us in our common tasks. There is a limitless number of sectors that will benefit from the technology, especially as the complexity of potential applications continues to grow.
[/et_pb_text][/et_pb_column][/et_pb_row][/et_pb_section]How Do Video Analytics Work?
Basic analytics use rule-based algorithms that follow a decision tree of “if/then” questions to predict whether an object in a video could be a possible threat, after a process of elimination, if the software has decided the incident is a threat, it generates alerts and/or triggers recording of the event.
Video analytic software isn’t watching a continuous stream of video. Instead, it is isolating and analyzing individual frames in sequence. Depending on the algorithm, the software will ask multiple questions of each image and applying the if/then rules until it is able to determine whether the condition is a true alarm or not.
Central Processing vs. Edge Processing
Video analytic software can be run centrally on servers on premise which is known as central processing or embedded into the camera itself which is known as edge processing.
Recent advances with camera SOC (System on a Chip) such as Ambarella, have increased processing power, allowing higher accuracy analytics possible directly on the camera.
By using video analytics on the edge, the video is processed and analyzed prior to streaming to recording server (either on premise or in the cloud) resulting in increased bandwidth and storage savings.
Edge Recording
Given the rise of analytics and increased storage directly on the camera, edge recording has become a reality. Now using cameras such as Digital Watchdog’s CAAS (Camera as a System) camera’s, a year of video can now be easily stored directly on the camera eliminating the need for a central recording server. In addition, archives could be permanently stored in the cloud for a completely redundant, economical solution.
Common Types of Video Analytics
Motion Detection: Video motion detection (VMD) is the most basic video analytic. It looks for pixel changes within the video to determine if motion is present.
Line Crossing: Detect people of objects crossing the detection line.
Counting Line: Calculate the number of people or objects crossing the detection line.
Loitering: Detect people or objects entering the detection zone but not exiting after a set duration.
Object Removal: Detect in real-time objects removed from the detection zone.
LPR (License Plate Recognition): Detect and record license plate numbers
Facial Recognition: Detect and recognize faces
Conclusion
Utilizing video analytics can provide significant savings in time and money. This sub-segment of the industry will be growing exponentially over the next 5 years and will be a significant game changer for the security industry.
The use of video analytics will be ubiquitous in our daily lives and invaluable in helping us in our common tasks. There is a limitless number of sectors that will benefit from the technology, especially as the complexity of potential applications continues to grow.
[/et_pb_text][/et_pb_column][/et_pb_row][/et_pb_section]What Are Video Analytics?
Video analytics are software applications that automatically analyze video content. It does this by using complex algorithms that process video to carry out a specific task. The software generates descriptions (metadata) of what is happening in the video. This information can list people, cars and other objects detected in the video stream, as well as their appearance and movements.
Why Would You Want Video Analytics?
As you might suspect, recording video for surveillance applications produces massive amounts of recorded video. Because of this, we can use video analytics to reduce the amount of video streamed and/or recorded so the user can be not only notified of a potential threat or situation, but whether a recording should be started in the first place. By using analytics, a cost and time savings can be realized.
How Do Video Analytics Work?
Basic analytics use rule-based algorithms that follow a decision tree of “if/then” questions to predict whether an object in a video could be a possible threat, after a process of elimination, if the software has decided the incident is a threat, it generates alerts and/or triggers recording of the event.
Video analytic software isn’t watching a continuous stream of video. Instead, it is isolating and analyzing individual frames in sequence. Depending on the algorithm, the software will ask multiple questions of each image and applying the if/then rules until it is able to determine whether the condition is a true alarm or not.
Central Processing vs. Edge Processing
Video analytic software can be run centrally on servers on premise which is known as central processing or embedded into the camera itself which is known as edge processing.
Recent advances with camera SOC (System on a Chip) such as Ambarella, have increased processing power, allowing higher accuracy analytics possible directly on the camera.
By using video analytics on the edge, the video is processed and analyzed prior to streaming to recording server (either on premise or in the cloud) resulting in increased bandwidth and storage savings.
Edge Recording
Given the rise of analytics and increased storage directly on the camera, edge recording has become a reality. Now using cameras such as Digital Watchdog’s CAAS (Camera as a System) camera’s, a year of video can now be easily stored directly on the camera eliminating the need for a central recording server. In addition, archives could be permanently stored in the cloud for a completely redundant, economical solution.
Common Types of Video Analytics
Motion Detection: Video motion detection (VMD) is the most basic video analytic. It looks for pixel changes within the video to determine if motion is present.
Line Crossing: Detect people of objects crossing the detection line.
Counting Line: Calculate the number of people or objects crossing the detection line.
Loitering: Detect people or objects entering the detection zone but not exiting after a set duration.
Object Removal: Detect in real-time objects removed from the detection zone.
LPR (License Plate Recognition): Detect and record license plate numbers
Facial Recognition: Detect and recognize faces
Conclusion
Utilizing video analytics can provide significant savings in time and money. This sub-segment of the industry will be growing exponentially over the next 5 years and will be a significant game changer for the security industry.
The use of video analytics will be ubiquitous in our daily lives and invaluable in helping us in our common tasks. There is a limitless number of sectors that will benefit from the technology, especially as the complexity of potential applications continues to grow.
[/et_pb_text][/et_pb_column][/et_pb_row][/et_pb_section]
What Are Video Analytics?
Video analytics are software applications that automatically analyze video content. It does this by using complex algorithms that process video to carry out a specific task. The software generates descriptions (metadata) of what is happening in the video. This information can list people, cars and other objects detected in the video stream, as well as their appearance and movements.
Why Would You Want Video Analytics?
As you might suspect, recording video for surveillance applications produces massive amounts of recorded video. Because of this, we can use video analytics to reduce the amount of video streamed and/or recorded so the user can be not only notified of a potential threat or situation, but whether a recording should be started in the first place. By using analytics, a cost and time savings can be realized.
How Do Video Analytics Work?
Basic analytics use rule-based algorithms that follow a decision tree of “if/then” questions to predict whether an object in a video could be a possible threat, after a process of elimination, if the software has decided the incident is a threat, it generates alerts and/or triggers recording of the event.
Video analytic software isn’t watching a continuous stream of video. Instead, it is isolating and analyzing individual frames in sequence. Depending on the algorithm, the software will ask multiple questions of each image and applying the if/then rules until it is able to determine whether the condition is a true alarm or not.
Central Processing vs. Edge Processing
Video analytic software can be run centrally on servers on premise which is known as central processing or embedded into the camera itself which is known as edge processing.
Recent advances with camera SOC (System on a Chip) such as Ambarella, have increased processing power, allowing higher accuracy analytics possible directly on the camera.
By using video analytics on the edge, the video is processed and analyzed prior to streaming to recording server (either on premise or in the cloud) resulting in increased bandwidth and storage savings.
Edge Recording
Given the rise of analytics and increased storage directly on the camera, edge recording has become a reality. Now using cameras such as Digital Watchdog’s CAAS (Camera as a System) camera’s, a year of video can now be easily stored directly on the camera eliminating the need for a central recording server. In addition, archives could be permanently stored in the cloud for a completely redundant, economical solution.
Common Types of Video Analytics
Motion Detection: Video motion detection (VMD) is the most basic video analytic. It looks for pixel changes within the video to determine if motion is present.
Line Crossing: Detect people of objects crossing the detection line.
Counting Line: Calculate the number of people or objects crossing the detection line.
Loitering: Detect people or objects entering the detection zone but not exiting after a set duration.
Object Removal: Detect in real-time objects removed from the detection zone.
LPR (License Plate Recognition): Detect and record license plate numbers
Facial Recognition: Detect and recognize faces
Conclusion
Utilizing video analytics can provide significant savings in time and money. This sub-segment of the industry will be growing exponentially over the next 5 years and will be a significant game changer for the security industry.
The use of video analytics will be ubiquitous in our daily lives and invaluable in helping us in our common tasks. There is a limitless number of sectors that will benefit from the technology, especially as the complexity of potential applications continues to grow.
[/et_pb_text][/et_pb_column][/et_pb_row][/et_pb_section]This article will give you a basic understanding of what video analytics are and what they can do for your business.
What Are Video Analytics?
Video analytics are software applications that automatically analyze video content. It does this by using complex algorithms that process video to carry out a specific task. The software generates descriptions (metadata) of what is happening in the video. This information can list people, cars and other objects detected in the video stream, as well as their appearance and movements.
Why Would You Want Video Analytics?
As you might suspect, recording video for surveillance applications produces massive amounts of recorded video. Because of this, we can use video analytics to reduce the amount of video streamed and/or recorded so the user can be not only notified of a potential threat or situation, but whether a recording should be started in the first place. By using analytics, a cost and time savings can be realized.
How Do Video Analytics Work?
Basic analytics use rule-based algorithms that follow a decision tree of “if/then” questions to predict whether an object in a video could be a possible threat, after a process of elimination, if the software has decided the incident is a threat, it generates alerts and/or triggers recording of the event.
Video analytic software isn’t watching a continuous stream of video. Instead, it is isolating and analyzing individual frames in sequence. Depending on the algorithm, the software will ask multiple questions of each image and applying the if/then rules until it is able to determine whether the condition is a true alarm or not.
Central Processing vs. Edge Processing
Video analytic software can be run centrally on servers on premise which is known as central processing or embedded into the camera itself which is known as edge processing.
Recent advances with camera SOC (System on a Chip) such as Ambarella, have increased processing power, allowing higher accuracy analytics possible directly on the camera.
By using video analytics on the edge, the video is processed and analyzed prior to streaming to recording server (either on premise or in the cloud) resulting in increased bandwidth and storage savings.
Edge Recording
Given the rise of analytics and increased storage directly on the camera, edge recording has become a reality. Now using cameras such as Digital Watchdog’s CAAS (Camera as a System) camera’s, a year of video can now be easily stored directly on the camera eliminating the need for a central recording server. In addition, archives could be permanently stored in the cloud for a completely redundant, economical solution.
Common Types of Video Analytics
Motion Detection: Video motion detection (VMD) is the most basic video analytic. It looks for pixel changes within the video to determine if motion is present.
Line Crossing: Detect people of objects crossing the detection line.
Counting Line: Calculate the number of people or objects crossing the detection line.
Loitering: Detect people or objects entering the detection zone but not exiting after a set duration.
Object Removal: Detect in real-time objects removed from the detection zone.
LPR (License Plate Recognition): Detect and record license plate numbers
Facial Recognition: Detect and recognize faces
Conclusion
Utilizing video analytics can provide significant savings in time and money. This sub-segment of the industry will be growing exponentially over the next 5 years and will be a significant game changer for the security industry.
The use of video analytics will be ubiquitous in our daily lives and invaluable in helping us in our common tasks. There is a limitless number of sectors that will benefit from the technology, especially as the complexity of potential applications continues to grow.