A COMBINED HISTOGRAM OF ORIENTED GRADIENTS AND COMPLETED LOCAL BINARY PATTERN METHODS FOR PEOPLE COUNTING IN A DENSE CROWD SCENARIO - PARDIANSYAH, INDRATNO (2015)
Estimating the number of people in a dense crowd scenario is one of the most interesting subjects in visual surveillance system application. It is extremely important in controlling and monitoring the crowd for safety control and urban planning. However, estimating the number of people in any dense crowd situation is not an easy task. This problem mostly arises due to some false positive and false negative and it affects the performance of system on detection rate. Therefore in this thesis, an innovative method for people counting in dense crowd scenario is proposed. This method used a collaborative Histogram of Oriented Gradients (HOG) and Completed Local Binary Pattern (CLBP) based on people detection algorithm to detect headshoulder region. Head-shoulder region is used as features to detect people against the false positive and false negative issue. HOG and CLBP descriptors are utilized to extract the edge contour and texture features of head-shoulder region, respectively. The two features are then fused together to generate a cumulative feature vectors. Support Vector Machine (SVM) is used to perform classification of the fusion features to people from a mixture of objects. The results show that the detection rate of the proposed method HOG-CLBP, on Recall value and Accuracy, achieves better performance compared to the current method for dense crowd scenario.