基于优化粒子群算法的运动视频目标跟踪算法

Yi Xu, Qiaomei Liang
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引用次数: 1

摘要

随着计算机技术的飞速发展和传感器性能的不断提高,体育视频目标跟踪技术在智能体育赛事、国际体育场馆安防监控系统等实际应用中的作用日益凸显。它已成为当前计算机运动视觉和人工智能领域的研究热点和难点。为了在复杂环境下实现对目标的长期、稳定、准确、高效的跟踪,建立有效的自适应模型并研究体育视频目标跟踪算法具有重要意义。本文的目的是研究基于优化粒子群算法的运动视频目标跟踪算法。提出了一种基于优化粒子的粒子滤波视频目标跟踪算法。该算法在粒子滤波框架的基础上,利用粒子群优化算法对重采样前的粒子进行优化,使权值较大的粒子保持静止不动,使权值较小的粒子不断接近权值较大的粒子,使大部分粒子处于高似然区域,从而降低重采样过程中的粒子消除率,改善粒子短缺问题。本文在算法过程框架层面采用了基于最小二乘的轨迹预测介入粒子滤波算法,使得改进后的跟踪算法在处理目标遮挡时,特别是在长期遮挡和背景突变的情况下,具有更强的鲁棒性。实验研究表明,在第27帧,由于目标被前景人暂时遮挡,当目标从被遮挡的前景后面重新出现时,无法正确跟踪目标,而前景遮挡被错误地视为目标进行跟踪迭代,导致跟踪过程失败,因为目标一直向前移动,而前景目标保持相对静止;因此跟踪误差百分比逐渐增大。
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Sports Video Target Tracking Algorithm Based on Optimized Particle Swarm Algorithm
With the rapid development of computer technology and the continuous improvement of sensor performance, the role of sports video target tracking technology in practical applications such as intelligent sports competitions and international sports stadium security monitoring systems has become increasingly prominent. It has become the current research hotspot and difficulty in the field of computer sports vision and artificial intelligence. In order to achieve long-term, stable, accurate and efficient tracking of targets in complex environments, it is of great significance to establish an effective adaptive model and research on sports video target tracking algorithms. The purpose of this paper is to study the sports video target tracking algorithm based on the optimized particle swarm algorithm. This paper proposes a particle filter video target tracking algorithm based on optimized particles. Based on the particle filter framework, this algorithm optimizes the particles before resampling by using the particle swarm optimization algorithm, keeping particles with larger weights still, and allowing particles with smaller weights to continuously approach particles with larger weights, so that most of the particles are in the high-likelihood area, thereby reducing the particle elimination rate in the re-sampling process and improving the problem of particle shortage. This paper uses the least squares-based trajectory prediction intervening particle filter algorithm at the level of the algorithm process framework, which makes the improved tracking algorithm more robust in dealing with target occlusion, especially in the case of long-term occlusion and background mutation. Experimental research shows that at the 27th frame, because the target is temporarily occluded by the foreground person, when the target reappears from behind the occluded foreground, the target cannot be tracked correctly, but the foreground occlusion is mistakenly regarded as the target for tracking iterations leading to the tracking process Failure, because the target keeps moving forward while the foreground target remains relatively stationary, so the tracking error percentage gradually increases.
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