Tracking multiple pedestrians across multi-camera scenarios is an important part of intelligent video surveillance and has great potential application for public security, which has been an attractive topic in the literature in recent years. In most previous methods, artificial features such as color histograms, HOG descriptors and Haar-like feature were adopted to associate objects among different cameras. But there are still many challenges caused by low resolution, variation of illumination, complex background and posture change. In this paper, a feature extraction network named NCA-Net is designed to improve the performance of multiple objects tracking across multiple cameras by avoiding the problem of insufficient robustness caused by hand-crafted features. The network combines features learning and metric learning via a Convolutional Neural Network (CNN) model and the loss function similar to neighborhood components analysis (NCA). The loss function is adapted from the probability loss of NCA aiming at object tracking. The experiments conducted on the NLPR_MCT dataset show that we obtain satisfactory results even with a simple matching operation. In addition, we embed the proposed NCA-Net with two existing tracking systems. The experimental results on the corresponding datasets demonstrate that the extracted features using NCA-net can effectively make improvement on the tracking performance.
Keywords: deep learning; metric learning; multi-camera; multi-object tracking.