We propose a methodology to detect keypoint sets on 3D point clouds. The final keypoint sets are detected by evaluating the properties of HoNO and the neighborhood covariance matrix. The detected keypoint sets offer better repeatability than others on 3D keypoint detection benchmark datasets. We propose a method to detect keypoint the world is flat keypoints pdf on 3D point clouds.
It is reliable to have sets of keypoints at high curvature and more informative areas rather than having a single keypoint that might sometimes arise due to noise. The proposed algorithm has two well-defined steps for keypoint sets detection. Secondly, the keypoint sets are detected from the salient regions by evaluating the properties of both the HoNO and the neighborhood covariance matrix. Through extensive experiments on publicly available benchmark datasets, it is shown that the detected keypoint sets offer better repeatability than those by the existing ones.
Check if you have access through your login credentials or your institution. This paper has been recommended for acceptance by Chennai Guest Editor. A novel crowd counting system is proposed which utilizes both keypoint-based features and segment-based features together. The foreground segmentation scheme designed for this system works without having to estimate the background image. New statistical features extracted from keypoints to capture some clues regarding to the complexity of the crowds is introduced.
Crowd counting is performed in a local manner rather than holistic level which results in a quite generalizable system. The counting of the number of people within a scene is a practical machine vision task, and it has been considered as an important application for security purposes. Most of the people counting algorithms generally extract the foreground segments and map the number of people to some features such as foreground area, texture, or edge count. Keypoint-based approaches, on the other hand, have also been proposed, which involves the use of statistical features of keypoints, such as the number of moving keypoints to estimate the crowd size. In contrast to the foreground segment-based methods, keypoint-based approaches are not sensitive to background changes, illuminations, occlusions, and shadows.
However, they have limited performance due to the lack of sufficient features. However, the whole approach is based on the keypoints and not all the image pixels. The proposed method, firstly, extracts the salient keypoints in the scene. Various features are extracted from each foreground segment together with the corresponding keypoints which are highly correlated with the size, density, and occlusion level of the crowd. Finally, a combination of the segment-based and keypoint-based features is used to estimate the number of people in crowds. The experiment demonstrates that the proposed method achieves lower counting error rates compared to the existing approaches.
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Computer Science and Technology from Zhejiang University, Hangzhou, China, in 2013. He joined CCNT Biometrics Lab of Zhejiang University, in 2009. Since 2013, he has been with the Faculty of Information Technology and Computer Engineering, Azarbaijan Shahid Madani University, where he is currently an assistant professor and the director of Artificial Intelligence Lab. His research interests include Visual Surveillance, Computer Vision, Artificial Intelligence, Machine Learning and Pattern Recognition. Computer Science from Zhejiang University, Hangzhou, China, in 2012. He is currently an assistant professor at the Faculty of IT and Computer Engineering of Azarbaijan Shahid Madani University, and the director of Machine Vision and Pattern Recognition Lab. His research interests include Machine Vision, Machine Learning, AI and Cognitive Science.
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