From Moving Objects Detection to Classification and Recognition: A Review for Smart Environments

Mathematical tools, machine learning, and signal processing tools have achieved enormous success in computer vision. In this chapter, we present state-of-the-art progress that has occurred in moving objects detection, classification, and recognition in video sequences taken by fixed cameras. More specifically, we focus on the latest breakthroughs made by deep learning (DL) that can be used for smart cities. The corresponding computer vision pipeline allows us to detect humans and vehicles for smart homes and cities. First, we survey developments in the field of moving objects detection using background subtraction techniques. Second, approaches that have been proposed to classify the extracted moving objects are also reviewed and classified into supervised, semi-supervised, and unsupervised learning methods. More specifically, they cover convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), autoencoder (AE), deep belief networks (DBN), and generative adversarial networks (GANs). Third, this chapter reviews approaches in the field of face recognition to identify people previously extracted using background subtraction and classified using deep neural networks (DNNs). Thus, this chapter aims to provide a comprehensive review of the segmentation, classification, and recognition of moving objects (humans, vehicles, etc.), covering conventional and recent advances.