In view of the problem that the typical convolutional neural networks fail to model actions at their full temporal extent, a novel video action recognition algorithm, which is based on improved 3D Convolutional Network (iC3D) architecture with K-means keyframes extraction and sparse representation classification (SRC), is proposed in this study. During the feature extraction process, the K-means keyframes extraction is constrained to reduce redundant information generated by continuous video frames and increase the temporal acceptance region. Meanwhile, to improve the noise immunity, sparse coding and its reconstruction errors are used for classification. The proposed method has 96.5% recognition accuracy on the typical video action classification dataset UCF101 that outperforms other competing methods. In addition, we built a wild test dataset to verify the generalization performance of the proposed model.
The instance segmentation for obstacle detection based on machine vision and deep learning is quite important for autonomous driving system. In this paper, a method using the Mask R-CNN based on feature fusion of RGB and depth images for instance segmentation is proposed. It extracts the features of depth image by designing a two-layer NiN network, and uses convolution to realize the feature fusion and dimension reduction of RGB image and depth image. The edge texture in depth image can improve the accuracy of boundary frame positioning. Experimental results on typical benchmark dataset demonstrates the effectiveness of the proposed method, which can improve the segmentation accuracy by 4% and the recall rate by 2%.
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