scholarly journals A novel approach to control the Jointed Arm Robot by tracking the position of the moving object on a conveyor using an Integrated Computer Vision system

2021 ◽  
Author(s):  
Pramod Kumar Thotapalli ◽  
◽  
CH. R. Vikram Kumar ◽  
B.ChandraMohana Reddy ◽  
◽  
...  
Keyword(s):  
Computer Vision ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Moving Objects ◽  
Vision System ◽  
Autonomous Systems ◽  
Gaussian Mixture ◽  
Vital Role ◽  
Work Cell ◽  
Novel Approach

Computer vision algorithms play a vital role in developing self-sustained autonomous systems. The objective of the present work is to integrate the robotic system with a moving conveyor using a single camera by adopting a Gaussian Mixture Model (GMM) based background subtraction method. In this work, a simple web camera is placed above the work cell to capture the continuous images of the moving objects on the conveyor along with a jointed arm robot are connected to a microcontroller through the computer. The position of the object with time and its features are extracted from the captured image frames by subtracting its background using the Gaussian Mixture Model (GMM). The output images of GMM are further processed by image processing techniques to extract the features like shape, color, center coordinates. The extracted coordinates of objects of interest are used as input parameters to the controller to activate the base rotation of a joint arm robot to perform different manipulations. The developed algorithm is evaluated on an indigenously fabricated work cell integrated with a computer vision setup.

Moving Objects Detection Algorithm Based on Three-Dimensional Gaussian Mixture Codebook Model Using XYZ Color Model

2013 ◽  
Vol 347-350 ◽  
pp. 3505-3509 ◽  
Author(s):  
Jin Huang ◽  
Wei Dong Jin ◽  
Na Qin
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Moving Objects ◽  
Three Dimensional ◽  
Gaussian Mixture ◽  
Detection Algorithm ◽  
Color Model ◽  
Moving Objects Detection ◽  
Objects Detection ◽  
Model Algorithm

In order to reduce the difficulty of adjusting parameters for the codebook model and the computational complexity of probability distribution for the Gaussian mixture model in intelligent visual surveillance, a moving objects detection algorithm based on three-dimensional Gaussian mixture codebook model using XYZ color model is proposed. In this algorithm, a codebook model based on XYZ color model is built, and then the Gaussian model based on X, Y and Z components in codewords is established respectively. In this way, the characteristic of the three-dimensional Gaussian mixture model for the codebook model is obtained. The experimental results show that the proposed algorithm can attain higher real-time capability and its average frame rate is about 16.7 frames per second, while it is about 8.3 frames per second for the iGMM (improved Gaussian mixture model) algorithm, about 6.1 frames per second for the BM (Bayes model) algorithm, about 12.5 frames per second for the GCBM (Gaussian-based codebook model) algorithm, and about 8.5 frames per second for the CBM (codebook model) algorithm in the comparative experiments. Furthermore the proposed algorithm can obtain better detection quantity.

scholarly journals Adaptive threshold for moving objects detection using gaussian mixture model

2020 ◽  
Vol 18 (2) ◽  
pp. 1122
Author(s):  
Moch Arief Soeleman ◽  
Aris Nurhindarto ◽  
Muslih Muslih ◽  
Karis W. ◽  
Muljono Muljono ◽  
...  
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Moving Objects ◽  
Gaussian Mixture ◽  
Adaptive Threshold ◽  
Moving Objects Detection ◽  
Objects Detection

Moving Objects Detection Based on Gaussian Mixture Model and Saliency Map

2011 ◽  
Vol 63-64 ◽  
pp. 350-354 ◽  
Author(s):  
Li Li Lin ◽  
Neng Rong Chen
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Moving Objects ◽  
Visual Saliency ◽  
Gaussian Mixture ◽  
Saliency Map ◽  
Dynamic Background ◽  
Moving Objects Detection ◽  
Saliency Maps ◽  
Objects Detection

The background modeling method based on the Gaussian mixture model (GMM) is usually used to detect the moving objects in static background. But when applied to dynamic background, for example caused by camera jitter, the wrong detection rate of moving objects is high, and thus affects the follow-up tracking. In addition, the method with GMM can not effectively remove the moving objects shadow region. This paper proposes a moving object detection method based on GMM and visual saliency maps, which not only can remove the disturbance caused by camera jitter, but also can effectively solve the shadow problem and achieve stable moving objects detection.

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