Multiple Moving Object Recognitions in video based on Log Gabor-PCA Approach

Object recognition in the video sequence or images is one of the sub-field of computer vision. Moving object recognition from a video sequence is an appealing topic with applications in various areas such as airport safety, intrusion surveillance, video monitoring, intelligent highway, etc. Moving object recognition is the most challenging task in intelligent video surveillance system. In this regard, many techniques have been proposed based on different methods. Despite of its importance, moving object recognition in complex environments is still far from being completely solved for low resolution videos, foggy videos, and also dim video sequences. All in all, these make it necessary to develop exceedingly robust techniques. This paper introduces multiple moving object recognition in the video sequence based on LoG Gabor-PCA approach and Angle based distance Similarity measures techniques used to recognize the object as a human, vehicle etc. Number of experiments are conducted for indoor and outdoor video sequences of standard datasets and also our own collection of video sequences comprising of partial night vision video sequences. Experimental results show that our proposed approach achieves an excellent recognition rate. Results obtained are satisfactory and competent.Comment: 8,26,conferenc

Moving Object Recognition Using Wavelets and Learning of Eigenspaces

This paper proposes a method for recognizing moving objects, which is based on a wavelet decomposition technique and learning of eigenspaces. High frequency vectors for reference image sequences are constructed using a wavelet decomposition formula. These high frequency vectors express the characteristics of moving objects in the image sequences. From them, a covariance matrix is made and eigenvectors of this matrix are computed. Some parametric eigenspaces are learnt by these eigenvectors. Next, orthogonal projections of the reference image sequences on the parametric eigenspaces are produced. To recognize a moving object included in an unknown image sequence, a high frequency vector and its orthogonal projections on the parametric eigenspaces are constructed. The recognition is carried out by computing the distance between the projection for the unknown image sequence and that for the reference image sequences

Moving Object Recognition Using Wavelets and Learning of Eigenspaces

This paper proposes a method for recognizing moving objects, which is based on a wavelet decomposition technique and learning of eigenspaces. High frequency vectors for reference image sequences are constructed using a wavelet decomposition formula. These high frequency vectors express the characteristics of moving objects in the image sequences. From them, a covariance matrix is made and eigenvectors of this matrix are computed. Some parametric eigenspaces are learnt by these eigenvectors. Next, orthogonal projections of the reference image sequences on the parametric eigenspaces are produced. To recognize a moving object included in an unknown image sequence, a high frequency vector and its orthogonal projections on the parametric eigenspaces are constructed. The recognition is carried out by computing the distance between the projection for the unknown image sequence and that for the reference image sequences