Teknik Rektifikasi Citra dan Tapis Kalman Dalam Mengestimasi Kecepatan Kendaraan

Estimating is a challenging task when the image sequence from a camera are directly processed because there is perspective projection that causes length and area ratio of objects in the image are not preserved. In this paper, it was used image rectification technique and Kalman filter algorithm to overcome the problems encountered in order to obtain accurate vehicle velocity estimation. Rectified images as result of image rectification were processed, then Kalman filter algorithm was executed based on the processing result of the rectified images. The result of the tests showed that geometric distortion on the objects in the image sequence could be corrected well by using image rectification. Kalman filter algorithm was also good enough in estimating vehicle velocity. The error of average velocity estimation was ±3 km/hour.Keywords : Estimation, Vehicle velocity, Image rectification, Kalman filter. AbstrakEstimasi kecepatan akan sulit dilakukan dengan langsung mengolah runtun citra kendaraan yang diperoleh dengan menggunakan kamera. Hal ini dikarenakan panjang maupun luas objek-objek di dalam citra mengalami perubahan akibat adanya proyeksi perspektif.Dalam penelitian ini, distorsi geometrik objek di dalam runtun citra akan diperbaiki dengan melakukan rektifikasi citra. Selanjutnya, algoritma tapis Kalman dijalankan guna mengolah informasi dari rectified images yang merupakan hasil rektifikasi runtun citra sehingga kecepatan kendaraan yang diamati dapat diperkirakan.Distorsi geometrik objek di dalam runtun citra yang diakibatkan oleh proyeksi perspektif pada kamera dapat dikoreksi dengan baik menggunakan rektifikasi citra yang dilakukan dengan menerapkan matriks transformasi proyektif yang dihitung berdasarkan kondisi (panjang maupun besar sudut) sebenarnya dari garis-garis objek di dalam runtun citra.Galat estimasi kecepatan rata-rata kendaraan ialah sebesar ±3 km/jam (saat kendaraan bergerak lurus).Kata Kunci : Estimasi, Kecepatan kenderaan, Rektifikasi citra dan Filter Kalman 

Tracking using a local closed-world assumption : tracking in the football domain

Thesis (M.S.)--Massachusetts Institute of Technology, Program in Media Arts & Sciences, 1994.Includes bibliographical references (leaves 81-85).by Stephen Sean Intille.M.S

A multiple-SIMD architecture for image and tracking analysis

The computational requirements for real-time image based applications are such as to warrant the use of a parallel architecture. Commonly used parallel architectures conform to the classifications of Single Instruction Multiple Data (SIMD), or Multiple Instruction Multiple Data (MIMD). Each class of architecture has its advantages and dis-advantages. For example, SIMD architectures can be used on data-parallel problems, such as the processing of an image. Whereas MIMD architectures are more flexible and better suited to general purpose computing. Both types of processing are typically required for the analysis of the contents of an image. This thesis describes a novel massively parallel heterogeneous architecture, implemented as the Warwick Pyramid Machine. Both SIMD and MIMD processor types are combined within this architecture. Furthermore, the SIMD array is partitioned, into smaller SIMD sub-arrays, forming a Multiple-SIMD array. Thus, local data parallel, global data parallel, and control parallel processing are supported. After describing the present options available in the design of massively parallel machines and the nature of the image analysis problem, the architecture of the Warwick Pyramid Machine is described in some detail. The performance of this architecture is then analysed, both in terms of peak available computational power and in terms of representative applications in image analysis and numerical computation. Two tracking applications are also analysed to show the performance of this architecture. In addition, they illustrate the possible partitioning of applications between the SIMD and MIMD processor arrays. Load-balancing techniques are then described which have the potential to increase the utilisation of the Warwick Pyramid Machine at run-time. These include mapping techniques for image regions across the Multiple-SIMD arrays, and for the compression of sparse data. It is envisaged that these techniques may be found useful in other parallel systems