论文标题
通过平衡优化器对多个对象进行实时检测免费跟踪
Real Time Detection Free Tracking of Multiple Objects Via Equilibrium Optimizer
论文作者
论文摘要
多个对象跟踪（MOT）是一项艰巨的任务，因为它通常需要特殊的硬件和更高的计算复杂性。在这项工作中，我们通过使用平衡优化器（EO）算法并减少对象的边界框的分辨率来解决对象的分辨率，以在检测自由框架中解决此类问题。首先，在第一帧中，目标对象是初始化并计算出其大小的，然后如果其高于阈值，则将其分辨率降低，然后由其内核颜色直方图建模以建立特征模型。对象模型的直方图和其他候选者之间的Bhattacharya距离用作要优化的健身函数。根据要跟踪的目标对象的数量，由EO生成多个代理。与全球优化的其他算法相比，EO算法的使用是由于其效率和较低的计算成本。实验结果证实，EO多对象跟踪器可实现满足其他跟踪器的满足跟踪结果。
Multiple objects tracking (MOT) is a difficult task, as it usually requires special hardware and higher computation complexity. In this work, we present a new framework of MOT by using of equilibrium optimizer (EO) algorithm and reducing the resolution of the bounding boxes of the objects to solve such problems in the detection free framework. First, in the first frame the target objects are initialized and its size is computed, then its resolution is reduced if it is higher than a threshold, and then modeled by their kernel color histogram to establish a feature model. The Bhattacharya distances between the histogram of object models and other candidates are used as the fitness function to be optimized. Multiple agents are generated by EO, according to the number of the target objects to be tracked. EO algorithm is used because of its efficiency and lower computation cost compared to other algorithms in global optimization. Experimental results confirm that EO multi-object tracker achieves satisfying tracking results then other trackers.
