Model-based mesh-to-grid image resampling with application to robust object detection, recognition and tracking

基于模型的网格到网格图像重采样，应用于稳健的对象检测、识别和跟踪

• 批准号: 402837983
• 负责人: Professor Dr.-Ing. André Kaup
• 金额: --
• 依托单位: Lehrstuhl für Multimediakommunikation und Signalverarbeitung
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2018
• 资助国家: 德国
• 起止时间: 2017-12-31 至 2020-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/402837983?language=en
• 关键词: Model; based; mesh; grid; image

Detection, recognition and tracking of objects are common tasks in video processing and they are widely used in various fields ranging from surveillance and medical imaging to automotive and entertainment industry. These tasks are, however, highly sensitive to image degradations such as low resolution input data, noise or optical and perspective distortions. In fact, when such common image conditions are faced, the performance of detection, recognition and tracking is severely deteriorated and it may also completely fail. To tackle this problem, input frames are typically preprocessed in order to counteract the effects of these adverse image conditions. First, input frames are registered. This registration typically operates with sub-pixel accuracy so the registered samples are located at arbitrary non-integer positions, called mesh. Hence, in a second step, mesh-to-grid resampling is applied. It consists of reconstructing the pixels on the regular 2D grid from the available mesh of samples and it conditions the resulting quality. Current solutions for resampling show significant low-pass behaviour and tend to introduce various visual artefacts which produce considerable and even irrecoverable errors during detection, recognition and tracking. Therefore, the goal of this project is to develop a robust high-quality mesh-to-grid resampling technique in order to leverage object detection, recognition and tracking. We take advantage of the so called frequency selective mesh-to-grid resampling algorithm which is an iterative procedure that generates an image model using a set of suitable basis functions. This technique exhibits an excellent performance in an ideal error-free environment. However, the performance of this algorithm drops drastically in real world scenarios where estimation errors during registration are involved. These scenarios shall be analysed by theoretical considerations in order to develop mechanisms to deal with these errors, to stabilise the output and to make it robust against noisy input data. The developed mesh-to-grid resampling technique will be tested for various detection, recognition and tracking applications. We expect to boost the performance of detection, recognition and tracking and make them correctly function in scenarios where other resamplers currently lead to failure. Finally, the obtained algorithms as well as the simulation framework will be made available to the scientific community in form of a software toolbox.

目标的检测、识别和跟踪是视频处理中的常见任务，它们广泛应用于从监控和医学成像到汽车和娱乐行业的各个领域。然而，这些任务对图像劣化高度敏感，例如低分辨率输入数据、噪声或光学和透视失真。事实上，当面对这样常见的图像条件时，检测、识别和跟踪的性能严重恶化，也可能完全失败。为了解决这个问题，通常对输入帧进行预处理，以抵消这些不利图像条件的影响。首先，登记输入帧。这种配准通常以子像素精度操作，因此配准的样本位于任意非整数位置，称为网格。因此，在第二步中，应用网格到网格的重新排序。它包括从样本的可用网格重建规则2D网格上的像素，并调节所得质量。目前的解决方案，resolution显示出显着的低通行为，并往往会引入各种视觉伪影，产生相当大的，甚至是不可恢复的错误，在检测，识别和跟踪。因此，该项目的目标是开发一种强大的高质量网格到网格的重新定位技术，以利用对象检测，识别和跟踪。我们利用所谓的频率选择性网格重建算法，这是一个迭代过程，使用一组合适的基函数生成一个图像模型。该技术在理想的无差错环境中表现出优异的性能。然而，该算法的性能急剧下降，在真实的世界的场景中，在注册过程中的估计误差参与。应通过理论考虑对这些情景进行分析，以便制定处理这些错误的机制，稳定输出并使其对噪声输入数据具有鲁棒性。开发的网格重新定位技术将进行测试，用于各种检测，识别和跟踪应用。我们希望提高检测，识别和跟踪的性能，并使它们在其他重采样器当前导致失败的情况下正确运行。最后，所获得的算法以及模拟框架将以软件工具箱的形式提供给科学界。