A Novel System for Nighttime Vehicle Detection Based on Foveal Classifiers With Real-Time Performance

Vehicle monitoring using camera networks is an important task for traffic applications. Moreover, it becomes critical in nighttime, when the probability of an accident considerably increases as visibility conditions worsen. Typical approaches are mostly based on the assumption that regions delimiting vehicle lights are well defined, so that they are segmented and then associated to vehicle entities. However, this assumption fails in images acquired by existing traffic camera networks, where vehicle lights are revealed as flashes and other complex light patterns, occupying large and even disconnected image regions. In this work, a real-time vehicle detection algorithm for nighttime situations has been presented, which is able to locate vehicles in the image by analyzing the previous complex light patterns. For this purpose, a novel machine learning framework based on a grid of foveal classifiers has been designed. Every classifier in the grid processes the same global image descriptor (only one descriptor is computed per image). However, every one of them is trained to predict a different output depending on the classifier position in the grid and the vehicle ground-truth location. Additionally, only point-based annotations are required to train the grid of foveal classifiers, speeding up the cost of creating the required databases. Experimental results prove the effectiveness of the proposed method in a new created nighttime database with point-based annotations.