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Öğe Features and Regression Techniques for Crowd Density Estimation: A Comparison(Ieee, 2019) Kurnaz, Oğuzhan; Hanilçi, CemalCrowd density estimation is an important problem for the security applications and it is a regression task consisting of feature extraction and regression steps. In this paper, we compare different features and regression techniques for crowd density estimation. To this end 200 images randomly selected from UCSD pedestrian dataset is used in the experiments. Experimental results show that features extracted from gray level co-occurance matrix (GLCM) gives the best performance however the selection of the regression technique depends on the performance criterion. Applying perspective normalization as a pre-processing step and feature elimination as a post-processing step considerably improve the performance.Öğe Multi-image Crowd Counting Using Multi-column Convolutional Neural Network(Springer, 2022) Kurnaz, Oğuzhan; Hanilçi, CemalCrowd density estimation is an important task for security applications. It is a regression problem consisting of feature extraction and estimation steps. In this study, we propose to use a modified version of previously introduced multi-column convolutional neural network (MCNN) approach for estimating crowd density. While in the original MCNN approach the same input image is applied to the each column of the network, we first propose to apply a different version of the same input image to extract a different mapping from each column. Second, original MCNN first generates an estimated density map and then performs crowd counting. Therefore, we adopt it for crowd counting and compare its performance with the proposed method. Regression task is performed by support vector regression (SVR) using feature vectors obtained from MCCNN. 2000 images selected from UCSD pedestrian dataset are used in the experiments. The regions of interest (ROI) are filtered out and the pixel values at the remaining regions are set to zero. In order to prevent distortion caused by camera position, perspective normalization has been applied as a pre-processing step which dramatically improves the performance.Öğe Multi-Image Crowd Density Estimation using Multi Column Deep Neural Network(Institute of Electrical and Electronics Engineers Inc., 2020) Kurnaz, Oğuzhan; Hanilçi, CemalCrowd density estimation is a challenging problem for security applications which is a regression task consisting of feature extraction and regression steps. In this paper, multicolumn deep neural networks (MDNN) is proposed for crowd density estimation. Regression task is performed using the feature vector obtained from MDNN. 2000 images selected from the UCSD pedestrian dataset are used in the experiments. In the images, the region of interest (ROI) is filtered and the remaining parts are removed. In order to avoid distortions due to camera position, perspective normalization is applied as a pre-processing step which yields considerable performance improvement. © 2020 IEEE.Öğe Real-time Implementation of Image Based PLC Control for a Robotic Platform(2019) Ayten, K. K.; Kurnaz, OğuzhanIn this study, image based real-time control of a linear robotic platform was performed. This robotic platform is used to determine the location of the mushroom and to direct the linear platform to the detected position in real time with PLC control. Haar-Cascade classifier was used to detect mushroom position and Visual Studio C # .NET platform was used to test the Cascade classifier and write other evaluation codes. One of the most important outputs of this work is to determine the actual position in the global coordinate from the pixel-based location of the object in the image using an ordinary USB camera or built-in camera. Calibration technique was used for this determination.
