Real-time video object detection and classification using hybrid texture feature extraction

In video processing, feature extraction and classification are necessary steps to classify the video frames. To improve accuracy, an efficient texture-based feature extraction is required. Also, before improving the feature extraction, the background subtraction step is almost equal to the ground truth level. An efficient real-time video object recognition and classification utilizing hybrid texture feature extraction are proposed. A stationary wavelet transform-based joint bilateral filtering is used to remove the noise. An effective background subtraction is employed before extracting the features. In feature extraction, hybrid texture feature extraction is proposed where gray level co-occurrence matrix (GLCM) and discrete wavelet transform (DWT) features are combined and applied to the categorization process. The GLCM is used to extract the second-order statistical texture features for the estimation of motion in the videos. And, the DWT algorithm reduces the storage space in real-time video processing. The main objectives are to implement the real-time video object detection and classification using the hybrid texture feature extraction method called the GLCM-DWT technique, and investigate the performance evaluation of the proposed methodology and compare with the existing technique. The parameters, namely accuracy, sensitivity, specificity, and execution time are to be evaluated for the proposed algorithm in the MATLAB software. © 2018 Informa UK Limited, trading as Taylor & Francis Group.