A monocular visual multilevel detection algorithm for multimotion targets for ARM isomorphic processors

The need for real-time processing of multi-frame images with complex geometric and optical corrections makes the algorithm more computationally intensive and requires higher processor performance. In order to ensure the real-time and resource consumption problems of the multi-motion target monocular vision multi-level detection algorithm in practical applications, a multi-motion target monocular vision multi-level detection algorithm for ARM isomorphic processors is proposed. The algorithm groups multiple cores in an isomorphic multicore processor evenly, so that the processor cores in the same group are a processor cluster set. The tasks to be scheduled are arranged in ascending order of utilization, and the tasks are assigned to each processor cluster set based on the worst-case adaptation algorithm, and it is guaranteed that the tasks with greater utilization than in each cluster set do not exceed the number of cores in the cluster set. In addition, the moving targets are separated from the background using methods such as background subtraction, inter-frame differencing, or optical flow. The detected targets are matched with their positions in the preceding and following frames to track the motion trajectory. Detection is performed at different resolution levels to handle targets of different sizes and velocities. The experimental results show that the algorithm can utilize the computing resources of the processor more efficiently, achieve faster target detection and tracking, and meet the needs of real-time applications.