DFT: A deep feature‐based semi‐supervised collaborative training for vehicle recognition in smart cities

Abstract

Intelligent transport systems (ITS) is a popular field of research in smart city. Image-based vehicle recognition is one of the most promising new techniques. The traditional appearance-based method has some limitations in feature view. The machine learning algorithm can satisfy multiple feature views, but it mainly adopts an improved supervised learning method, which needs manual annotation and has low efficiency. The semisupervised collaborative training method is widely used in the image recognition process to improve machine learning algorithms' accuracy and generalization performance. This paper proposes DFT (deep feature-based training) method for vehicle recognition in smart cities. DFT is also a semi-supervised collaborative training method on basis of two base learners. DFT adjusts data pre-processing and training process, optimizes the constructing a disagreement encoding network, and expands the recognition disagreement of pseudo-labelled samples-based training set. Compared with the typical collaborative training methods, DFT greatly accelerates the model's training process by reducing the convergence time, and improves the efficiency of vehicle recognition, while remaining the recognition accuracy unchanged.