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Swimmer’s Head Detection Based on a Contrario and Scaled Composite JTC Approaches
International Journal of Optics ( IF 1.8 ) Pub Date : 2020-04-01 , DOI: 10.1155/2020/4145938
D. Benarab 1, 2 , T. Napoléon 1 , A. Alfalou 1 , A. Verney 2 , P. Hellard 3
Affiliation  

In order to accompany the swimming coaches in evaluating high-level swimmers, we developed a prototype for instantaneous speed estimation. To achieve this, we proposed and validated, in a previous work, a swimmer tracking system based on data fusion. However, the initialization phase is done manually, and our aim, in this paper, is to automate this process. First, we propose a region of interest localization module that allows the detection of the first appearance of the swimmer in the lane as well as the restriction of the region of interest around him. This module is based on the method a contrario which consists of modeling the random noise corresponding to the water and detecting the structured movement relative to the swimmer motion. To do that, we calibrate the pool using DLT (Direct Linear Transform) technique, extract the concerned lane, apply the frame difference approach to detect the moving objects, and then decompose the lane into blocs and classify them into swimmer motion or noise. Second, in order to detect the swimmer’s head, we propose the Scaled Composite JTC which is based on the NL-JTC correlation technique. The input plane of this latter includes a target and a reference image. The first is the region of interest detected by the method a contrario. The second consists of a Scaled Composite Reference. The tests conducted on real video sequences of French swimming championships (Limoges 2015) showed very good results in terms of region of interest localization and swimmer’s head detection which allows a reliable initialization for the tracking system.

中文翻译:

基于对比度和缩放复合JTC方法的游泳者头部检测

为了陪同游泳教练评估高级游泳者,我们开发了一种用于瞬时速度估算的原型。为了实现这一目标,我们在先前的工作中提出并验证了基于数据融合的游泳者追踪系统。但是,初始化阶段是手动完成的,因此本文的目的是使此过程自动化。首先,我们提出了一个感兴趣区域定位模块,该模块可以检测泳者在泳道中的首次出现以及对他周围感兴趣区域的限制。该模块基于一种方法,该方法包括对与水相对应的随机噪声进行建模并检测相对于游泳者运动的结构化运动。为此,我们使用DLT(直接线性变换)技术校准泳池,提取相关车道,应用帧差方法检测运动物体,然后将车道分解为块并将其分类为游泳者运动或噪声。其次,为了检测游泳者的头部,我们提出了基于NL-JTC相关技术的Scaled Composite JTC。后者的输入平面包括目标和参考图像。第一个是通过该方法检测到的感兴趣区域。第二个由比例综合参考构成。在法国游泳锦标赛的真实视频序列上进行的测试(Limoges,2015年)在关注区域定位和游泳者头部检测方面显示出非常好的结果,从而可以对跟踪系统进行可靠的初始化。然后将泳道分解为群体并将其分类为游泳者的运动或噪音。其次,为了检测游泳者的头部,我们提出了基于NL-JTC相关技术的Scaled Composite JTC。后者的输入平面包括目标和参考图像。第一个是通过该方法检测到的感兴趣区域。第二个由比例综合参考构成。在法国游泳锦标赛的真实视频序列上进行的测试(Limoges,2015年)在关注区域定位和游泳者头部检测方面显示出非常好的结果,从而可以对跟踪系统进行可靠的初始化。然后将泳道分解为群体并将其分类为游泳者的运动或噪音。其次,为了检测游泳者的头部,我们提出了基于NL-JTC相关技术的Scaled Composite JTC。后者的输入平面包括目标和参考图像。第一个是通过该方法检测到的感兴趣区域。第二个由比例综合参考构成。在法国游泳锦标赛的真实视频序列上进行的测试(Limoges,2015年)在关注区域定位和游泳者头部检测方面显示出非常好的结果,从而可以对跟踪系统进行可靠的初始化。后者的输入平面包括目标和参考图像。第一个是通过该方法检测到的感兴趣区域。第二个由比例综合参考构成。在法国游泳锦标赛的真实视频序列上进行的测试(Limoges,2015年)在关注区域定位和游泳者头部检测方面显示出非常好的结果,从而可以对跟踪系统进行可靠的初始化。后者的输入平面包括目标和参考图像。第一个是通过该方法检测到的感兴趣区域。第二个由比例综合参考构成。在法国游泳锦标赛的真实视频序列上进行的测试(Limoges,2015年)在关注区域定位和游泳者头部检测方面显示出非常好的结果,从而可以对跟踪系统进行可靠的初始化。
更新日期:2020-04-01
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