Convolutional neural networks have achieved great success in computer vision, significant improving the state of the art in image classification, semantic segmentation, object detection and face recognition. In this chapter, we illustrate the advance made by the convolutional neural network (CNN) in surveillance and security applications using two examples. For the surveillance application, a novel military object detector called Deep Fusion Detector was proposed, which incorporates information fusion techniques and the CNN. Specifically, we fused multi-channel images within a CNN to enhance the significance of deep features, and adapted a state-of-the-art generic object detector for military scenario. For the security application, with inspiration from recent advances in the deep learning community, we presented an effective face recognition system called Deep Residual Face. Where the Inception-ResNet CNN architecture was utilized to extracting deep features and the center loss function was adopted for training the face verification network. The extensive experiments showed the effectiveness of the presented methods.
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