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Object detection using Metaheuristic algorithm for volley ball sports application

机译:对象检测使用凌空球体育应用的群体训练算法

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摘要

Object Detection has been a great challenge over the years. The reason behind is that, it is applied for numerous real time applications like vision based control, traffic control, video surveillance, sports analysis, etc. But, object detection in a video sequence is a highly challenging task. It has various problems like occlusion, fast moving objects, shadow, poor lighting, color contrast and other static background objects. This reason brought the object detection to be a thrust research area in the field of image processing. In the previous researches the conventional methods of object detection like Frame Difference, Mixture of Gaussian (MoG), Optical Flow etc., still have the above problems. Hence the research focuses on a different approach in object detection using Metaheuristic algorithm for the video sequence of volley ball player in the practice session. In this research three Metaheuristic Algorithms, namely Firefly, Teaching and Learning Based Optimization (TLBO) and Cuckoo Search Algorithm are used. These algorithms are evaluated and compared with the parameters like accuracy, precision, and recall. The result shows Cuckoo Search Algorithm is best suited to object detection especially in this application.
机译:对象检测多年来是一个巨大的挑战。背后的原因是,它适用于许多实时应用,如视觉基于视觉的控制,流量控制,视频监控,体育分析等。但是,视频序列中的对象检测是一个高度具有挑战性的任务。它有各种各样的问题,如遮挡,快速移动的物体,阴影,照明,颜色对比和其他静态背景物体。这一原因将物体检测带来了图像处理领域的推力研究区域。在先前的研究中,对象检测的传统方法,如帧差,高斯(沼泽),光学流量等的混合仍然具有上述问题。因此,研究侧重于使用练习会话中的凌轮球员视频序列的综合算法对象检测中的不同方法。在这项研究中,使用了三种成群质算法,即萤火虫,教学和基于学习的优化(TLBO)和Cuckoo搜索算法。评估这些算法,并将其与准确度,精度和召回等参数进行比较。结果显示杜鹃搜索算法最适合对象检测,特别是在本申请中。

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