ABSTRACT Object tracking is being utilized in the field of computer vision over decades for video surveillance, human–computer interaction and robotic applications. Even though the state-of-the-art tracking technology is rapidly growing, few issues are still challenging such as illumination variation, pose variation, scale changes, occlusion, etc. Among these challenges, sudden illumination variation is more complicated which is not solved completely. Most of the current trackers, indeed work under controlled illumination conditions in outdoor and indoor environments. In this work, we study the effect of adding the photometric normalization techniques prior to tracking in order to minimize the drift during abrupt light changes of the median flow tracker (MFT). The tracker under investigation is based on the optical flow method and achieved remarkable results in the tracking literature. However, it drifts off during sudden illumination variation. To resolve this problem, pre-processing technique is incorporated just before tracking. Hence, we present an experimental study of various pre-processing techniques to improve the accuracy of the MFT. A total of eight state-of-the-art normalization techniques are summarized and tested in video tracking perspective. The experiments are carried out with the video sequences obtained from the object tracking benchmark dataset posing sudden illumination change as a challenge to analyze the modified tracker. A comparative analysis indicates that the modified tracker outperforms the baseline tracker in terms of precision score and overlap score.

中文翻译：

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视频跟踪视角中光照不变技术的比较研究

摘要 几十年来，目标跟踪在计算机视觉领域被用于视频监控、人机交互和机器人应用。尽管最先进的跟踪技术正在迅速发展，但仍然存在很少的问题，如光照变化、姿态变化、尺度变化、遮挡等。在这些挑战中，突然的光照变化更为复杂，没有解决完全地。大多数当前的跟踪器确实在室外和室内环境中的受控照明条件下工作。在这项工作中，我们研究了在跟踪之前添加光度归一化技术的效果，以最大限度地减少中值流量跟踪器 (MFT) 突然光线变化期间的漂移​​。正在研究的跟踪器基于光流方法，并在跟踪文献中取得了显着的成果。然而，它会在突然的光照变化期间漂移。为了解决这个问题，在跟踪之前加入了预处理技术。因此，我们对各种预处理技术进行了实验研究，以提高 MFT 的准确性。从视频跟踪的角度总结和测试了总共八种最先进的归一化技术。实验是使用从对象跟踪基准数据集获得的视频序列进行的，这些视频序列将突然的光照变化作为分析修改后的跟踪器的挑战。比较分析表明，修改后的跟踪器在精度得分和重叠得分方面优于基线跟踪器。