To solve the curse of dimensionality problem in multi-agent reinforcement learning, a learning method based on k-means is presented in this paper. In this method, the environmental state is represented as key state factors. The state space explosion is avoided by classifying states into different clusters using k-means. The learning rate is improved by assigning different states to existent clusters, as well as corresponding strategy. Compared to traditional Q-learning, our experimental results of the multi-robot cooperation show that our scheme improves the team learning ability efficiently. Meanwhile, the cooperation efficiency can be enhanced successfully.
The traditional background difference method is based on gray image. Some information is lost when color image is transformed into gray image. So it is difficult to discriminate different colors with similar gray values and easily disturbed by noise and shadows. In this paper, the background difference is based on RGB color model. It is proposed to use the average value of each pixel of the color image sequences to extract the background, and then use the three-dimensional color values of the current frame and background image to compute the difference to detect the moving objects. The proposed approach is simple and easy to implement. The experimental results show that it is more sensitive to colors and has higher accuracy and robustness than the traditional background difference method. Besides, it is more resistant to shadows.
The process of tracking objects in real time video is one of the important topics in the study of suveillance system (Dhananjaya, Rama, and Thimmaiah 2015). Detection and extraction of information and tracking of objects or moving objects is as one form of application from computer vision. Some applications that use the method of tracking objects or moving objects include UAV (Unmanned Aerial Vehicle) surveillance or better known as the engine / vehicle unmanned, Indoor Monitoring system is an indoor monitoring system, as well as monitoring traffic traffic that can observe the movement of all Objects in real time state. Tracking objects in real time state many things to note and need to be taken into account where all the parameters and noise or disturbance of the surrounding objects that we do not need to observe but still in one part with the object that we observe. In this research the method to be used is background subtraction For the detection of tracking object And moving objects in real time based on color By utilizing webcam cameras and using OpenCv opensource library. 1. Pendahuluan Dalam perkembangan teknologi informasi dan komunikasi modern muncul bidang yang mempelajari tentang computer vision. Computer vision merupakan salah satu bagian dari bidang teknologi informasi dan komunikasi yang merupakan perkembangan dari ilmu grafika serta pengolahan citra digital yang bergabung dengan berbagai ilmu bidang komputer yang di tujukan untuk meniru cara kerja dari pengelihatan manusia yang dapat menangkap berbagai informasi diantaranya geometri, warna, ukuran, warna dan interpretasi dari suatu obyek. Kajian di bidang Computer vision telah berkembang pesat di barbagai bidang diantaranya bagian militer, kesehatan, industri dan lain-lain degan banyak ditemukanya peralatan yang canggih yang di hasilkan dari kajian computer vision antara lain yang menyangkut biomatric seperti deteksi wajah orang, sidik jari, retina mata serta yang non biomatric seperti pengenalan barang dengan sinar x, deteksi plat nomor kendaraan, jenis dan ukuran kendaraaan serta banyak lagi hal lain yang ditemukan dari kajian mengenai Computer Vision (Szeliski 2010). Deteksi objek berdasarkan warna merupakan suatu kajian yang sangat menarik yang dapat di implementasikan kedalam berbagai kehidupan baik dunia industi misalnya dalam pendeteksian kematangan dan pensortiran buah-buahan berdasarkan warna, quality control secara otomatis pada industri kain, kertas dan sebagainya yang memerlukan pendeteksian berdasarkan warna adalah sangat banyak, meskipun tidak menutup kemungkinan dikembankan pada industri sensorik
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