Crowd density estimation based on texture feature extraction

As we know, feature extraction has an important role in crowd density estimation. In our paper, we introduce a new texture feature called Tamura, which is usually used in image retrieval algorithms. On the other hand, the time consuming is another issue that must be considered, especially for the real-time application of the crowd density estimation. In most methods, multiple features with high dimension such as the gray level co-occurrence matrix (GLCM) are used to construct the input feature vector, which will decrease the performance of the whole method. In order to solve the problem, we use Principal Component Analysis (PCA) method, which can obtain the mainly information of the feature using less dimension features. In the end, we use the Support Vector Machine (SVM) for estimating the crowd density. Experiments demonstrate that our method can generate high accuracy at low computational cost compared with other existing methods. © 2013 ACADEMY PUBLISHER.