Video processing solutions for motion analysis are key tasks in many computer vision applications, ranging from human activity recognition to object detection. In particular, speed estimation algorithms may be relevant in contexts such as street monitoring and environment surveillance. In most realistic scenarios, the projection of a framed object of interest onto the image plane is likely to be affected by dynamic changes mainly related to perspectival transformations or periodic behaviours. Therefore, advanced speed estimation techniques need to rely on robust algorithms for object detection that are able to deal with potential geometrical modifications. The proposed method is composed of a sequence of pre-processing operations, that aim to reduce or neglect perspetival effects affecting the objects of interest, followed by the estimation phase based on the Maximum Likelihood (ML) principle, where the speed of the foreground objects is estimated. The ML estimation method represents, indeed, a consolidated statistical tool that may be exploited to obtain reliable results. The performance of the proposed algorithm is evaluated on a set of real video recordings and compared with a block-matching motion estimation algorithm. The obtained results indicate that the proposed method shows good and robust performance.
翻译:移动分析的视频处理解决方案是许多计算机视觉应用中的关键任务,从人类活动识别到物体探测等,特别是速度估计算法可能与街道监测和环境监测等情况有关。在多数现实的假设中,对图像平面上感兴趣的一个框架对象的预测可能受到动态变化的影响,这些变化主要与透视变换或定期行为有关。因此,先进的速度估计技术需要依靠可靠的算法来探测能够处理可能的几何修改的物体。拟议方法由一系列预处理操作组成,目的是减少或忽视影响对象的永久性影响,然后是基于最大相似性原则的估计阶段,其次是预测地表物体的速度。ML估计方法确实代表一种综合统计工具,可以加以利用,以获得可靠的结果。拟议算法的性能是通过一套真实的录像记录来评价的,并与块对动估计算法进行比较。获得的结果表明,拟议的方法显示良好和健全的性能。