Storage system and method for object monitoring
A storage system and method for object monitoring/anticipation in surveillance systems are provided. In one embodiment, a storage system is provided comprising a memory and a controller. The controller is configured to identify positions of an object in a plurality of frames of video data provided by a video capture device; determine a rate of movement of the object based on the identified positions; and based on the determined rate of movement of the object, provide a suggestion to the video capture device to dynamically modify an encoding bit rate of the video data to improve video quality of the object. Other embodiments are provided.
1. A storage system comprising:
an interface configured to communicate with a computer system;
a memory; and
a controller in communication with the interface and the memory, wherein the controller is configured to:
identify positions of an object in a plurality of frames of video data provided by a video capture device in communication with the computer system;
determine a rate of movement of the object based on the identified positions;
based on the determined rate of movement of the object and an encoding bit rate of the video data, determine a number of frames that will contain the object before the object leaves a capture zone; and
in response to determining that the number of frames that will contain the object before the object leaves the capture zone is below a threshold, provide a suggestion to the video capture device to dynamically increase the encoding bit rate of the video data to increase the number of frames that will contain the object before the object leaves the capture zone to meet the threshold.
2. The storage system of claim 1 , wherein the controller is further configured to identify positions of the object at a beginning of a capture zone.
3. The storage system of claim 1 , wherein the controller is further configured to identify positions of the object during a first two frames that the object appears in the video data.
4. The storage system of claim 1 , wherein the controller is further configured to identify the object using machine learning.
5. The storage system of claim 1 , wherein the controller is further configured to identify a type of the object.
6. The storage system of claim 5 , wherein the controller is further configured to identify the type of the object using machine learning.
7. The storage system of claim 5 , wherein the type of the object comprises one of the following: a human, an animal, and a vehicle.
8. The storage system of claim 5 , wherein the controller is further configured to provide a suggestion to the video capture device to dynamically increase the encoding bit rate of the video data based on the type of the object.
9. The storage system of claim 1 , wherein the memory comprises a three-dimensional memory.
10. The storage system of claim 1 , wherein the controller is further configured to restore the encoding bit rate to its original value after the object leaves the capture zone.
11. The storage system of claim 1 , wherein the number of frames is increased to a predetermined number.
12. The storage system of claim 1 , wherein the number of frames is increased to meet a quality of service requirement.
13. The storage system of claim 1 , wherein controller is further configured to determine the rate of movement and provide the suggestion as the video data is being received.
14. The storage system of claim 1 , wherein controller is further configured to determine the rate of movement and provide the suggestion prior to the video data being stored in the memory.
15. The storage system of claim 1 , wherein the controller is further configured to:
identify an object type of the object; and
provide a suggestion to the video capture device to further modify the encoding bit rate of the video data based on the object type.
16. A method comprising:
performing the following in a storage system comprising a memory:
detecting a part of an object in a frame of video data provided by a video capture device, wherein the part of the object is detected in a first instantaneous decoder refresh (IDR) frame using machine learning;
performing interpolation on the detected part of the object to determine a type of the object;
determining a statically-estimated speed of the object based on its determined type;
based on the statically-estimated speed of the object and an encoding bit rate of the video data, determining a number of frames that will contain the object before the object leaves a capture zone; and
in response to determining that the number of frames that will contain the object before the object leaves the capture zone is below a threshold, providing a suggestion to the video capture device to increase the encoding bit rate to increase the number of frames to meet the threshold.
17. The method of claim 16 , wherein the type of the object comprises one of the following: a human, an animal, and a vehicle.
18. A device comprising:
means for identifying positions of an object in a plurality of frames of video data provided by a video capture device in communication with a computer system;
means for determining a rate of movement of the object based on the identified positions;
means for determining a number of frames that will contain the object before the object leaves a capture zone based on the determined rate of movement of the object and an encoding bit rate of the video data; and
means for providing a suggestion to the video capture device to dynamically increase the encoding bit rate of the video data to increase the number of frames that will contain the object before the object leaves the capture zone to meet the threshold, in response to determining that the number of frames that will contain the object before the object leaves the capture zone is below a threshold.