CN109669780B - Video analysis method and system - Google Patents
Video analysis method and system Download PDFInfo
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- CN109669780B CN109669780B CN201811589989.9A CN201811589989A CN109669780B CN 109669780 B CN109669780 B CN 109669780B CN 201811589989 A CN201811589989 A CN 201811589989A CN 109669780 B CN109669780 B CN 109669780B
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
- G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
- G06F9/5005—Allocation of resources, e.g. of the central processing unit [CPU] to service a request
- G06F9/5011—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resources being hardware resources other than CPUs, Servers and Terminals
- G06F9/5016—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resources being hardware resources other than CPUs, Servers and Terminals the resource being the memory
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
- G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
- G06F9/5005—Allocation of resources, e.g. of the central processing unit [CPU] to service a request
- G06F9/5011—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resources being hardware resources other than CPUs, Servers and Terminals
- G06F9/5022—Mechanisms to release resources
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Abstract
The invention discloses a video analysis method and a system, wherein the method comprises the following steps: when the video analysis task is detected to be suspended, if the current analysis task exits, detecting whether the exiting current analysis task and the suspended video analysis task are the same dimension analysis task; if yes, sending state keeping information to the current analysis task to maintain the current execution state, and executing the suspended video analysis task; if not, informing the current analysis task to quit execution, and releasing the GPU resources corresponding to the current analysis task. By the method, the GPU memory is fully utilized to execute a plurality of analysis tasks concurrently, repeated loading of the analysis models can be reduced by executing the tasks with the same dimensionality in succession, time waste caused by repeated loading is avoided, and video analysis efficiency and GPU utilization rate are improved.
Description
Technical Field
The present application relates to the field of video processing technologies, and in particular, to a video parsing method and system.
Background
The video image information structuring is an important technology, and aims to convert video information into a text structure and store the text structure in a database, so that later retrieval is facilitated and various applications are realized. In engineering, the video is analyzed, shots are separated, scenes or targets in the shots are detected and tracked, and finally a track flow of related information is formed. The AI technology at present mainly refers to deep learning, and since a single deep learning model can generally analyze information of only one dimension, such as an object and a scene, multiple model operations may be involved in a single video parsing process. Meanwhile, in addition to the deep learning model requiring a Graphic Processor (GPU), algorithms such as shot detection and tracking may also be operated by a CPU. In the single-video single-dimension single-instance parsing process, when arithmetic operations are executed on a CPU, the GPU is kept idle. To improve GPU top-efficiency, multiple dimensions of multiple models are typically maintained within the GPU, multitasking and parsing the video. Due to uncertainty of the analysis task, after the current analysis task is completed, different models may need to be loaded to perform the next analysis task, and the model used by the current analysis task is limited by the size of the GPU memory, and the GPU memory may need to be unloaded to be used by the next task model.
Therefore, in the existing video parsing method, a large amount of time is wasted when the GPU unloads and loads the model, and the video parsing efficiency is reduced.
Disclosure of Invention
The invention provides a video analysis method and a video analysis system, which are used for solving the problems that in the existing video analysis method, more time is wasted and the video analysis efficiency is reduced when a GPU unloads and loads a model.
The specific technical scheme is as follows:
a method of video parsing, the method comprising:
when the video analysis task is detected to be hung, judging whether the current analysis task exists or not to exit;
if yes, detecting whether the current analysis task to be exited is the same dimension analysis task as the suspended video analysis task;
if yes, sending state keeping information to the current analysis task to maintain the current execution state, and executing the suspended video analysis task;
if not, informing the current analysis task to quit execution, and releasing the GPU resources corresponding to the current analysis task.
Optionally, before determining whether the current parsing task exits, the method further includes:
initializing a GPU memory management server, and acquiring the number of GPUs and corresponding memory size information;
and acquiring a memory peak value corresponding to the analytic model, and storing the memory peak value, the GPU number and corresponding memory size information.
Optionally, before determining whether there is a current parsing task to exit, the method HIA includes:
receiving a video analysis task, and judging whether idle GPU resources exist at present to meet the video analysis task;
if so, calling an analysis model corresponding to the video analysis task, and entering an analysis state;
if not, the video analysis task is suspended, and the current video analysis task is waited to be finished.
Optionally, the determining whether there is an idle GPU resource satisfying the video parsing task currently includes:
and judging whether the idle memory of the current idle GPU meets the memory corresponding to the video analysis task.
A video analytics system, comprising:
the memory management module is used for judging whether the current analysis task exits or not when the video analysis task is detected to be hung; if yes, detecting whether the current analysis task to be exited is the same dimension analysis task as the suspended video analysis task; if yes, sending state keeping information to the current analysis task to maintain the current execution state, and executing the suspended video analysis task; if not, informing the current analysis task to quit execution, and releasing GPU resources corresponding to the current analysis task;
and the video analysis module is used for analyzing the video data.
Optionally, the memory management module is further configured to initialize the GPU memory management server, and obtain the number of GPUs and corresponding memory size information; and acquiring a memory peak value corresponding to the analytic model, and storing the memory peak value, the GPU number and corresponding memory size information.
Optionally, the memory management module is further configured to receive a video parsing task, and determine whether there is an idle GPU resource satisfying the video parsing task currently; if so, calling an analysis model corresponding to the video analysis task, and entering an analysis state; if not, the video analysis task is suspended, and the current video analysis task is waited to be finished.
Optionally, the memory management module is further configured to determine whether an idle memory of the currently idle GPU meets a memory corresponding to the video parsing task.
By the method, the GPU memory is fully utilized to execute a plurality of analysis tasks concurrently, repeated loading of the analysis models can be reduced by executing the tasks with the same dimensionality in succession, time waste caused by repeated loading is avoided, and video analysis efficiency and GPU utilization rate are improved.
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Fig. 1 is a flowchart of a video parsing method according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a video parsing system according to an embodiment of the present invention.
Detailed Description
The technical solutions of the present invention are described in detail with reference to the drawings and the specific embodiments, and it should be understood that the embodiments and the specific technical features in the embodiments of the present invention are merely illustrative of the technical solutions of the present invention, and are not restrictive, and the embodiments and the specific technical features in the embodiments of the present invention may be combined with each other without conflict.
Fig. 1 is a flowchart of a video parsing method according to an embodiment of the present invention, where the method includes:
s1, when detecting that the video analysis task is suspended, judging whether the current analysis task exits;
firstly, the method provided by the invention is applied to a model dynamic loading management system, the system comprises a GPU memory management module and a video analysis task program, the GPU memory management module and the video analysis task program are mutually independent processes, and the video analysis task program is started by the GPU memory management module. The two modules are interacted through local IPC, and when the analysis program exits, a message is sent to inform the GPU memory management module.
When the system is started, the GPU memory management server is initialized, and the number of GPUs and the corresponding memory size information are obtained. That is, determine how many GPUs are currently available and the memory corresponding to the GPUs. And acquiring a memory peak value corresponding to the analytic model, wherein the memory peak value indicates resources required by the analytic model. The GPU memory management module will store the above information.
When the GPU memory management module receives the video parsing task, it is determined whether there is a free GPU resource satisfying the video parsing task, that is, whether a free memory of the currently free GPU satisfies a memory corresponding to the video parsing task.
And if the memory meets the requirements, calling an analysis model corresponding to the video analysis task, and entering an analysis state.
And if the memory does not meet the requirements, suspending the video analysis task, namely, putting the video analysis task into the waiting queue again, waiting for the end of the current video analysis task, and matching the current video analysis task to the corresponding GPU resource.
When the current video analysis task is suspended, whether the current analysis task exits is judged, if not, S2 is executed, and if yes, S3 is executed.
S2, continuing to suspend the video analysis task;
s3, detecting whether the exiting current parsing task is the same dimension parsing task as the suspended video parsing task;
the system detects the video analysis task about to exit in real time, and when the current analysis task is about to exit, the system firstly judges whether the suspended video analysis task is the same dimension analysis task as the current analysis task, namely whether the analysis model required by the suspended video analysis task is the same as the analysis model of the current analysis task.
If yes, then S4 is executed, otherwise, then S5 is executed.
S4, sending state keeping information to the current analysis task to maintain the current execution state and execute the suspended video analysis task;
and S5, informing the current analysis task to quit execution and releasing the GPU resources corresponding to the current analysis task.
That is, the video task parser will continue parsing the suspended video task after the exiting current parsing task exits.
By the method, the GPU memory is fully utilized to concurrently execute a plurality of analysis tasks, repeated loading of analysis models can be reduced by sequentially executing the tasks with the same dimensionality, time waste caused by repeated loading is avoided, and video analysis efficiency and GPU utilization rate are improved.
Corresponding to the method provided by the present invention, an embodiment of the present invention further provides a video parsing system, and as shown in fig. 2, the video parsing system in the embodiment of the present invention is schematically configured, and the system includes:
the memory management module 201 is configured to, when it is detected that a video parsing task is suspended, determine whether a current parsing task exists and is to exit; if yes, detecting whether the current analysis task to be exited is the same dimension analysis task as the suspended video analysis task; if yes, sending state keeping information to the current analysis task to maintain the current execution state, and executing the suspended video analysis task; if not, informing the current analysis task to quit execution, and releasing GPU resources corresponding to the current analysis task;
and the video analysis module 202 is used for analyzing the video data.
Further, in the embodiment of the present invention, the memory management module 201 is further configured to initialize a GPU memory management server, and obtain the number of GPUs and corresponding memory size information; and acquiring a memory peak value corresponding to the analytic model, and storing the memory peak value, the GPU number and corresponding memory size information.
Further, in the embodiment of the present invention, the memory management module 201 is further configured to receive a video parsing task, and determine whether there is a free GPU resource currently satisfying the video parsing task; if so, calling an analysis model corresponding to the video analysis task, and entering an analysis state; if not, the video analysis task is suspended, and the current video analysis task is waited to be finished.
Further, in this embodiment of the present invention, the memory management module 201 is further configured to determine whether an idle memory of the currently idle GPU meets a memory corresponding to the video parsing task.
While the preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following appended claims be interpreted as including the preferred embodiment and all such alterations and modifications as fall within the scope of the application, including the use of specific symbols, labels, or other designations to identify the vertices.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.
Claims (8)
1. A method for video parsing, the method comprising:
when the video analysis task is detected to be hung, judging whether the current analysis task exists or not to exit;
if yes, detecting whether the current analysis task to be exited is the same dimension analysis task as the suspended video analysis task;
if yes, sending state keeping information to the current analysis task to maintain the current execution state, and executing the suspended video analysis task;
if not, informing the current analysis task to quit execution, and releasing the GPU resources corresponding to the current analysis task.
2. The method of claim 1, wherein prior to determining whether there is a current resolution task to exit, the method further comprises:
initializing a GPU memory management server, and acquiring the number of GPUs and corresponding memory size information;
and acquiring a memory peak value corresponding to the analytic model, and storing the memory peak value, the GPU number and corresponding memory size information.
3. The method of claim 2, wherein prior to determining whether there is a current resolution task to exit, the method further comprises:
receiving a video analysis task, and judging whether idle GPU resources exist at present to meet the video analysis task;
if so, calling an analysis model corresponding to the video analysis task, and entering an analysis state;
if not, the video analysis task is suspended, and the current video analysis task is waited to be finished.
4. The method of claim 3, wherein the determining whether there are currently idle GPU resources to satisfy the video parsing task specifically comprises:
and judging whether the idle memory of the current idle GPU meets the memory corresponding to the video analysis task.
5. A video parsing system, comprising:
the memory management module is used for judging whether the current analysis task exits or not when the video analysis task is detected to be hung; if yes, detecting whether the current analysis task to be exited is the same dimension analysis task as the suspended video analysis task; if yes, sending state keeping information to the current analysis task to maintain the current execution state, and executing the suspended video analysis task; if not, informing the current analysis task to quit execution, and releasing GPU resources corresponding to the current analysis task;
and the video analysis module is used for analyzing the video data.
6. The system of claim 5, wherein the memory management module is further configured to initialize the GPU memory management server, and obtain the number of GPUs and corresponding memory size information; and acquiring a memory peak value corresponding to the analytic model, and storing the memory peak value, the GPU number and corresponding memory size information.
7. The system of claim 5, wherein the memory management module is further configured to receive a video parsing task, and determine whether there are currently idle GPU resources to satisfy the video parsing task; if so, calling an analysis model corresponding to the video analysis task, and entering an analysis state; if not, the video analysis task is suspended, and the current video analysis task is waited to be finished.
8. The system of claim 5, wherein the memory management module is further configured to determine whether a free memory of the currently free GPU meets a memory corresponding to the video parsing task.
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