The super-resolution integrated imaging based on sparse camera array and convolution neural network can reduce the rendering time by reducing the number of cameras, and then reconstruct the low-resolution element image into highresolution element image by using convolutional neural network. In order to further improve the effect of element image reconstruction, this paper improves the network model optimizer and sensitive parameters, constructs activation function and loss function, and uses smaller convolution kernel in the last layer of convolution neural network to improve the quality of the generated element image. At last, the original scheme and the improved scheme are verified and compared through the TensorFlow platform. The experimental results show that the reconstruction element image generated by the improved scheme is better and the network training time is shorter.
This paper aims to achieve robust behavior recognition of video object in complicated background. Features of the video object are described and modeled according to the depth information of three-dimensional video. Multi-dimensional eigen vector are constructed and used to process high-dimensional data. Stable object tracing in complex scenes can be achieved with multi-feature based behavior analysis, so as to obtain the motion trail. Subsequently, effective behavior recognition of video object is obtained according to the decision criteria. What’s more, the real-time of algorithms and accuracy of analysis are both improved greatly. The theory and method on the behavior analysis of video object in reality scenes put forward by this project have broad application prospect and important practical significance in the security, terrorism, military and many other fields.
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