Moving Object Tracking Using Camshif and SURF Algorithm

Penjejakan objek bergerak (moving object tracking) sebagai sebuah permasalahan yang berperan penting dalam bidang computer vision dan secara luas dapat diterapkan dalam banyak aplikasi dunia nyata seperti pengawasan otomatis, human pose estimation, navigasi kendaraan, pemantauan lalu lintas, dan robot vision. Moving object tracking membutuhkan metode yang memiliki akurasi dan ketahanan yang baik terhadap Perubahan yang terjadi pada objek. Penelitian ini membangun sebuah aplikasi untuk membandingkan Algoritma Camshift (Continuosly Adaptive Mean-Shift) dan Algoritma SURF (Speeded Up Robust Feature). Aplikasi dapat melakukan penjejakan dengan menggunakan kedua metode sekaligus. Pengujian dilakukan dengan lima kondisi pergerakan objek yang berbeda pada tiga warna latar belakang berbeda untuk membandingkan waktu komputasi dan akurasi dari kedua metode. Hasil pengujian menunjukkan bahwa akurasi Camshift lebih baik dibanding Surf, sementara untuk waktu komputasi Surf mengungguli Camshift

You never surf alone. Ubiquitous tracking of users' browsing habits

In the early age of the internet users enjoyed a large level of anonymity. At the time web pages were just hypertext documents; almost no personalisation of the user experience was o ered. The Web today has evolved as a world wide distributed system following specific architectural paradigms. On the web now, an enormous quantity of user generated data is shared and consumed by a network of applications and services, reasoning upon users expressed preferences and their social and physical connections. Advertising networks follow users' browsing habits while they surf the web, continuously collecting their traces and surfing patterns. We analyse how users tracking happens on the web by measuring their online footprint and estimating how quickly advertising networks are able to pro le users by their browsing habits

Sparse optical flow regularisation for real-time visual tracking

Optical flow can greatly improve the robustness of visual tracking algorithms. While dense optical flow algorithms have various applications, they can not be used for real-time solutions without resorting to GPU calculations. Furthermore, most optical flow algorithms fail in challenging lighting environments due to the violation of the brightness constraint. We propose a simple but effective iterative regularisation scheme for real-time, sparse optical flow algorithms, that is shown to be robust to sudden illumination changes and can handle large displacements. The algorithm proves to outperform well known techniques in real life video sequences, while being much faster to calculate. Our solution increases the robustness of a real-time particle filter based tracking application, consuming only a fraction of the available CPU power. Furthermore, a new and realistic optical flow dataset with annotated ground truth is created and made freely available for research purposes

Note: An object detection method for active camera

To solve the problems caused by a changing background during object detection in active camera, this paper proposes a new method based on SURF (speeded up robust features) and data clustering. The SURF feature points of each image are extracted, and each cluster center is calculated by processing the data clustering of k adjacent frames. Templates for each class are obtained by calculating the histograms within the regions around the center points of the clustering classes. The window of the moving object can be located by finding the region that satisfies the histogram matching result between adjacent frames. Experimental results demonstrate that the proposed method can improve the effectiveness of object detection.Yong Chen, Ronghua Zhang, Lei Shang, and Eric H

Bio-Inspired Robotic Fish With Vision Based Target Tracking

The lionfish is an invasive species that out-competes and overcrowds native sh species along the eastern seaboard of the United States and down into the Caribbean. Lionfish populations are growing rapidly. Current methods of monitoring lionfish populations are costly and time intensive. A bio-inspired robotic fish was built to use as an autonomous lionfish tracking platform. Lionfish are tracked visually using an onboard processor. Five different computer vision methods for identification and tracking are proposed and discussed. These include: background subtraction, color tracking, mixture of Gaussian background subtraction, speeded up robust feature (SURF), and CamShift based tracking. Each of these methods were compared and their accuracy analyzed. CamShift based tracking is determined to be the most accurate for this application. Preliminary experiments for system identification and control design are discussed

Interest point detectors for visual SLAM

In this paper we present several interest points detectors and we analyze their suitability when used as landmark extractors for vision-based simultaneous localization and mapping (vSLAM). For this purpose, we evaluate the detectors according to their repeatability under changes in viewpoint and scale. These are the desired requirements for visual landmarks. Several experiments were carried out using sequence of images captured with high precision. The sequences represent planar objects as well as 3D scenes