An Operational System for Estimating Road Traffic Information from Aerial Images

Jens Leitloff, Dominik Rosenbaum, Franz Kurz, Oliver Meynberg, Peter Reinartz
2014 Remote Sensing  
Given that ground stationary infrastructures for traffic monitoring are barely able to handle everyday traffic volumes, there is a risk that they could fail altogether in situations arising from mass events or disasters. In this work, we present an alternative approach for traffic monitoring during disaster and mass events, which is based on an airborne optical sensor system. With this system, optical image sequences are automatically examined on board an aircraft to estimate road traffic
more » ... road traffic information, such as vehicle positions, velocities and driving directions. The traffic information, estimated in real time on board, is immediately downlinked to a ground station. The airborne sensor system consists of a three-head camera system, a real-time-capable GPS/INS unit, five industrial PCs and a downlink unit. The processing chain for automatic extraction of traffic information contains modules for the synchronization of image and navigation data streams, orthorectification and vehicle detection and tracking modules. The vehicle detector is based on a combination of AdaBoost and support vector machine classifiers. Vehicle tracking relies on shape-based matching operators. The processing chain is evaluated on a large number of image sequences recorded during several campaigns, and the data quality is compared to that obtained from induction loops. In summary, we can conclude that the achieved overall quality of the traffic data extracted by the airborne system is in the range of 68% and 81%. Thus, it is comparable to data obtained from stationary ground sensor networks. There are many approaches for vehicle detection from aerial images. Extensive overviews are given in [16] [17] [18] . Generally, vehicle detection is performed using implicit or explicit models. The first approaches with explicit models used a simple rectangular mask for detection [19, 20] . Later on, extensions to 3D wire fame models were introduced and combined with classification methods [21] . One of the most mature works using hierarchical vehicle models was shown in [22] , which was used for road verification. Most approaches are not concerned with computation time, which is a critical condition for real-time applications, such as the system proposed in this work. Implicit models in combination with neural networks were used by [23, 24] with promising results. In [25] , an online boosting procedure for efficient training data collection was utilized. They used different features, which can all be calculated very quickly using integral images or integral histograms. Finally, a non-parametric algorithm performs clustering of the calculated confidence values. An interesting work was presented in [26] . They introduced new features for vehicle detection, i.e., color probability maps and pairs of pixels. These led to very large feature sets, and partial least squares were used for feature transformation. The authors of [27] use rotation invariant histograms of oriented gradients [28] and an adaptive boosting classifier. Even though the authors use the same kind of imagery as the presented work (see Section 2.1), the overall approach is not in the operational state. In the last few years, there have been many approaches to vehicle detection working with images from UAVs [29] [30] [31] [32] . These systems have a small payload, which leads to limited coverage compared to airborne systems. Additionally, the regulatory framework to fly UAVs remains uncertain in some countries, while a concept for the country-wide operationalization of the presented system can be found in [33] . In recent decades, several methods for automatic tracking algorithms have been examined. The first results were achieved based on the optical flow [34, 35] from the image sequences of stationary ground traffic cams. Further developments in vehicle tracking were carried out by [36] , who used a deformable vehicle template model, and by [37] , who presented a 3D modeling approach. Airborne frame cameras with a low or medium frame rate require alternative methods. Fundamental research on this topic based on change detection algorithms was done by [38, 39] . While these algorithms work fine on moving objects, they are not suitable for recording vehicles that are static. In [6, 7] , detection-based tracking algorithms on a medium frame rate system were presented. There, tracking was performed by an intelligent attribution of vehicle detections in consecutive images. A similar approach was transferred to low frame rate sequences in [40] . Later on, the focus of development shifted to combined detection tracking approaches, in which the tracking was based on template matching (e.g., [8, 41, 42] ). The tracking results shown in this paper are based on the latter approaches. At the current operational state, all vehicle are tracked individually. Newer approaches for aerial vehicle tracking use more advanced prediction methods, such as Kalman filtering and, furthermore, track multiple objects simultaneously [43] . In [44], particle filtering for multiple vehicles is presented, which is currently integrated with the operational system. Due to the increased number of high resolution satellite imagery systems in the last decade, there have been many approaches for vehicle detection [45] [46] [47] [48] [49] and even tracking from single-pass images [50] [51] [52] . However, these systems are not comparable to the presented work. Satellites have revisit times of
doi:10.3390/rs61111315 fatcat:3bm5rav5endszm3m7wj2malwp4