Moving object recognition using multi view three dimensional convolutional neural networks
Moving object recognition (MOR) is an important but challenging problem in the field of computer vision. The aim of MOR is to recognize moving objects in a given video dataset. Convolutional neural networks (CNNs) have been extensively used for image recognition and video analysis problems. Recently, a 3D CNN, which contains 3D convolution layers, was proposed to address MOR problems by successfully extracting spatiotemporal features. In this paper, a multi view (MV) 3D CNN is proposed for MOR. This model combines 3D CNNs with a well known MV learning technique. Because multi view learning techniques have the ability to obtain more view related features from videos captured by different cameras, the proposed model can extract more representative features. Moreover, the model contains a special view pooling layer that can fuse the feature information from previous layers. The proposed MV3D CNN is applied to both real world moving vehicle recognition and sign language recognition tasks. The experimental results show that the proposed model possesses good performance.
Neural Computing and Applications