Video analysis and accelerating method based on thread level flow line

Abstract

The invention discloses a video analysis and accelerating method. The method comprises the following steps: dividing a video frame processing task into four levels of subtasks according a sequential order, and allocating the subtasks to GPU and CPU to process; realizing each level of subtask through a thread, transmitting data to a thread for the next subtask after processing, and ensuring that all the threads perform concurrent execution; pausing and waiting when new tasks does not exist or a thread for the next level of subtask does not accomplish processing; adopting a first-in first-out (FIFO) buffer queue to transmit data between threads for two adjacent levels of subtasks; realizing asynchronous cooperation concurrency of CPU and GPU subtasks through CUDA function asynchronous invocation for two subtask not in a dependency relationship. According to the method, various computing resources in a heterogeneous system are effectively utilized, a reasonable task scheduling mechanism is established, and communication overhead between different processors is reduced, so that the computing power of each computing resource is given into full play, and the system efficiency is improved.

Description

technical field [0001] The present invention relates to the field of video analysis, and more specifically, to a method for accelerating video analysis based on a thread-level pipeline. Background technique [0002] Video analysis technology has been widely used in the security field. There is a large amount of image data processing in video analysis, which leads to low efficiency. With the popularity of high-definition cameras, it is necessary to optimize the performance of video analysis systems. In recent years, most optimization techniques use GPU to process complex calculations in video analysis, convert serial algorithms of complex calculation modules into parallel algorithms, and use GPUs for parallel computing. However, the CPU and GPU are two processors with different architectures. Using a simple GPU optimization method, the CPU and GPU often wait for each other due to the different calculation time, resulting in waste of computing resources, and cannot ensure the ...

AI Technical Summary

Problems solved by technology

However, the CPU and GPU are two processors with different architectures. Using a simple GPU optimization method, the CPU and GPU often wait for each other due to the different calculation time, resulting in waste of computing resources, and cannot ensure the optimal utilization of the CPU and GPU. Unable to give full play to the computing power of heterogeneous platforms

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