移动摄像机的目标跟踪算法

B. Zalesky
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引用次数: 1

摘要

提出了一种运动摄像机跟踪目标的自适应颜色跟踪算法。该算法的特点之一是将被跟踪对象的特征集与当前帧的背景相适应。在每一步中,算法都会从目标特征中提取出那些对目标更具体,同时至少对当前帧背景更具体的特征,因为其余的目标特征不仅无助于跟踪目标与背景的分离,而且会阻碍目标的正确检测。物体和背景的特征是基于场景的颜色表示而形成的。它们可以用两种方式计算。第一种方法是通过著名的k-means算法的快速版本对目标和背景的聚类图像进行3d颜色向量。第二种方法是将rgb颜色空间划分为3d平行六面体,然后将每个像素的颜色替换为像素颜色属于同一平行六面体的所有颜色的平均值。该算法的另一个特点是它的简单性,这使得它可以在小型移动计算机上使用,比如Jetson TXT1或TXT2。该算法在各种摄像机捕获的视频序列上进行了测试,并使用了著名的TV77数据集,该数据集包含77种不同的标记视频序列。实验证明了该算法的有效性。在测试图像上,其精度和速度克服了在计算机视觉库OpenCV 4.1中实现的跟踪器的特点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Object tracking algorithm by moving video camera
The algorithm ACT (Adaptive Color Tracker) to track objects by a moving video camera is presented. One of the features of the algorithm is the adaptation of the feature set of the tracked object to the background of the current frame. At each step, the algorithm extracts from the object features those that are more specific to the object and at the same time are at least specific to the current frame background, since the rest of the object features not only do not contribute to the separation of the tracked object from the background, but also impede its correct detection. The features of the object and background are formed based on the color representations of scenes. They can be computed in two ways. The first way is 3D-color vectors of the clustered image of the object and the background by a fast version of the well-known k-means algorithm. The second way consists in simpler and faster partitioning of the RGB-color space into 3D-parallelepipeds and subsequent replacement of the color of each pixel with the average value of all colors belonging to the same parallelepiped as the pixel color. Another specificity of the algorithm is its simplicity, which allows it to be used on small mobile computers, such as the Jetson TXT1 or TXT2.The algorithm was tested on video sequences captured by various camcorders, as well as by using the well-known TV77 data set, containing 77 different tagged video sequences. The tests have shown the efficiency of the algorithm. On the test images, its accuracy and speed overcome the characteristics of the trackers implemented in the computer vision library OpenCV 4.1.
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