Improved Camshift with adaptive searching window

Abstract

Camshift is widely used real-time algorithm in video target tracking field. The size of searching window (SW) is a key factor of Camshift, and bigger or smaller size of SW will both decrease the real-time feature of Camshift. In this paper, a accelerated Camshift with adaptive searching window (ACASW) was proposed. Firstly the meanshift process and computational cost (CC) were modeled, and the relationship between the size of SW and CC was analyzed quantificationally, then the optimized size of SW was deduced, which was used in the proposed algorithm. From the experimental results it can be seen that, compared to the traditional Camshift with fixed EW, the proposed algorithm can reduce the computing time effectively, which improve the real-time feature of the algorithm

Face Tracking menggunakan algoritma Camshift untuk mendeteksi pergerakan murid dikelas

“…Pemanfaatan face tracking berperan penting dalam komputer vision dan secara luas dapat diterapkan dalam berbagai aplikasi seperti, pengawasan otomatis, pemantauan lalu lintas dan robot vision [2]. Face tracking bergerak sangat kompleks sehubungan dengan perubahan pergerakan yang fleksibel, perubahan cahaya dan perubahan sudut pandang [19]. Penggunaan pada pengawasan otomatis digunakan untuk dapat menemukan objek berupa wajah pada seseorang pada suatu ruangan.…”

“…Pastinya, kamera tentu hanya mengambil semua objek tubuh pada orang yang masuk ke ruangan tersebut, maka diperlukan pencarian wajah pada objek tubuh seseorang untuk dapat mengenali orang tersebut. Ada beberapa faktor yang mempengaruhi hasil dari face tracking yaitu adanya objek lain yang menutupi sebagian dari objek, latar belakang objek yang memiliki ciri dan warna yang sama [19] [22]. Maka di butuhkan suatu metode yang memiliki akurasi ketahanan yang baik terhadap perubahan yang terjadi pada objek.…”

Relative Pose Measurement Algorithm of Non-cooperative Target based on Stereo Vision and RANSAC

“…The method flow chart of relative pose measurement based on stereo vision proposed by this paper is shown as Figure 3. As for extracting of characteristic points, this paper adopted the method stated in reference [7]: Since most of the non-cooperative targets are cubes and quipped with attachment bracket, that is, there's at least one rectangular surface or triangular surface on the target, edges can be extracted through Canny edge detection algorithm in the left image, and edge linear equation can be obtained by using Hough Transformation, then, space characteristic points will be unveiled, and the points tracked in subsequent frames [17] . After the computer pixel coordinates of the space characteristic points (u 1 ,v 1 ) and(u 2 ,v 2 ) are located in both left and right images, the 3-D coordinates of the space characteristic points can be gained by solving equations (3) and (4) through least square method.…”

“…At present, there're following measuring methods for non-cooperative target: (1) Image Matching. It establishes model according to the actual satellite, and match the existing model with collected satellite image to identify the target pose [2][3] [17] . However, this method can be processed only when the shape and dimension of the satellite are known.…”

Fast and robust L0-tracker using compressive sensing

“…In contrast to the Meanshift algorithm, continuous adaptive distribution has been proposed in CAMshift algorithms. An accelerated CAMshift algorithm with adaptive searching windows is proposed in [4]. The optimized size of searching windows is obtained after modeling the Meanshift process and computational cost.…”