We describe a system for vehicle make and model recognition (MMR) that automatically detects and classifies the make and model of a car from a live camera mounted above the highway. Vehicles are detected using a histogram of oriented gradient detector and then classified by a convolutional neural network (CNN) incorporating the frontal view of the car. We propose a semiautomatic data-selection approach for the vehicle detector and the classifier, by using an automatic number plate recognition engine to minimize human effort. The resulting classification has a top-1 accuracy of 97.3% for 500 vehicle models. This paper presents a more extensive in-depth evaluation. We evaluate the effect of occlusion and have found that the most informative vehicle region is the grill at the front. Recognition remains accurate when the left or right part of vehicles is occluded. The small fraction of misclassifications mainly originates from errors in the dataset, or from insufficient visual information for specific vehicle models. Comparison of state-of-the-art CNN architectures shows similar performance for the MMR problem, supporting our findings that the classification performance is dominated by the dataset quality.
The growing traffic density in cities fuels the desire for collision assessment systems on public transportation. For this application, video analysis is broadly accepted as a cornerstone. For trams, the localization of tramway tracks is an essential ingredient of such a system, in order to estimate a safety margin for crossing traffic participants. Tramway-track detection is a challenging task due to the urban environment with clutter, sharp curves and occlusions of the track. In this paper, we present a novel and generic system to detect the tramway track in advance of the tram position. The system incorporates an inverse perspective mapping and a-priori geometry knowledge of the rails to find possible track segments. The contribution of this paper involves the creation of a new track reconstruction algorithm which is based on graph theory. To this end, we define track segments as vertices in a graph, in which edges represent feasible connections. This graph is then converted to a max-cost arborescence graph, and the best path is selected according to its location and additional temporal information based on a maximum a-posteriori estimate. The proposed system clearly outperforms a railway-track detector. Furthermore, the system performance is validated on 3,600 manually annotated frames. The obtained results are promising, where straight tracks are found in more than 90% of the images and complete curves are still detected in 35% of the cases.