Accelerative Object Classification Using Cascade Structure for Vision Based Security Monitoring Systems

Abstract

Nowadays, object detection systems have achieved significant results, and applied in many important tasks such as security monitoring, surveillance systems, autonomous systems, human- machine interaction and so on. However, one of the most challenges is limitation of computational processing time. In order to deal with this task, a method for speed up processing time is investigated in this paper. The binary of cascaded structural model based detection method is applied for security monitoring systems (SMS). The classification based on cascade structure has been shown advance in extremely rapid discarding negative samples. The SMS is constructed based on two main techniques. First, a feature descriptor for representing data of image based on the modified Histograms of Oriented Gradients (HOG) method is applied. This feature description method allows extracting huge set of partial descriptors, then filtering to obtain only high-discriminated features on training set. Second, the cascade structure model based on the SVM kernel is used for rapidly binary classifying objects. In order taking advantage of optimal SVM classification, the local descriptor within each block is used to feed to SVM. The number of SVMs in each classifier is depended on the precision rate, which decided at the training step. The experimental results demonstrate the effectiveness of this method variety of dataset.