Vehicle type classification and attribute prediction using multi task RCNN
Vehicle classification is an important subject of study due to its significance in a number of areas including law enforcement, traffic surveillance, autonomous navigation, and transportation management. While numerous approaches have been proposed, few studies have been published with regard to the multi view classification of vehicles captured in real surveillance. In this paper, we consider the multi view classification of vehicles as an attribute prediction problem with views (rear, front, and side) as attributes. The corresponding multi task learning is implemented in the Region based Convolutional Neural Network (RCNN) framework, which classifies vehicle categories (car, truck, bus, and van) and predicts the attributes simultaneously. Experiments on a field captured vehicle dataset provide satisfactory results, with approximate 83% accuracy for vehicle type classification and over 90% accuracy for attribute prediction.
2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP BMEI)