An Object Surveillance Algorithm Based on Batch-Normalized CNN and Data Augmentation in Smart Home

Smart home technology powered by cutting-edge intelligent algorithms creates a secure and comfortable living environment for electricity end users. In this paper, we propose an object surveillance algorithm and apply it to smart home environment. A combination usage of batch-normalized convolutional neural network (CNN) and data augmentation technique has been introduced for accomplishing object surveillance task by overseeing an object for long duration. Three aspects are considered including algorithm convergence, model creation, and dataset preparation. Firstly, mathematical demonstrations are offered to study the vanishing gradient phenomenon, and batch normalization is proposed for resolving this issue. Secondly, a revised version of AlexNet with batch normalization layer added after each convolution layer significantly boosts training speed and retains test set accuracy. Thirdly, only one picture taken on-premise is required for dataset preparation, and satisfactory predictive results are obtained.