The discrete wavelet transform can be found at the heart of many image-processing algorithms. Until now, the transform on general-purpose processors (CPUs) was mostly computed using a separable lifting scheme. As the lifting scheme consists of a small number of operations, it is preferred for processing using single-core CPUs. However, considering a parallel processing using multi-core processors, this scheme is inappropriate due to a large number of steps. On such architectures, the number of steps corresponds to the number of points that represent the exchange of data. Consequently, these points often form a performance bottleneck. Our approach appropriately rearranges calculations inside the transform, and thereby reduces the number of steps. In other words, we propose a new scheme that is friendly to parallel environments. When evaluating on multi-core CPUs, we consistently overcome the original lifting scheme. The evaluation was performed on 61-core Intel Xeon Phi and 8-core Intel Xeon processors.
.We proposed an innovative stereo camera pair calibration method suitable for traffic surveillance applications. In the method, we first split the sought parameters into two groups. The first group contains parameters that can be computed prior to the device being installed in the desired location, and we determine their values using already established methods. The second group contains parameters that need to be evaluated after the device is installed on-site, and we utilize calibration vehicles for their evaluation. We first localize the calibration vehicles by detecting and tracking their license plates in a series of stereo images; afterward, we exploit the known information about their speed and acceleration to compute the distances that they traveled between two frames; finally, we determine the internal and external stereo camera pair parameters by minimizing the difference between the computed and estimated distances while preserving the epipolar geometry. We evaluated the presented method on a task of measuring the distances that vehicles traveled between two consecutive frames. For this evaluation, we recorded a dataset containing almost 700 vehicles with trajectories that were recorded using prototype hardware and ground truth distance measurements that were obtained from a pair of single-beam LIDARs. The evaluation results were compared with the current state-of-the-art methods for long-distance stereo camera pair calibration and used in the application of the proposed method on the vehicle speed measurement task.
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