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Road traffic monitoring is an imperative topic widely discussed among researchers. Systems used to monitor traffic frequently rely on cameras mounted on bridges or roadsides. However, aerial images provide the flexibility to use mobile platforms to detect the location and motion of the vehicle over a larger area. To this end, different models have shown the ability to recognize and track vehicles. However, these methods are not mature enough to produce accurate results in complex road scenes. Therefore, this paper presents an algorithm that combines state-of-the-art techniques for identifying and tracking vehicles in conjunction with image bursts. The extracted frames were converted to grayscale, followed by the application of a georeferencing algorithm to embed coordinate information into the images. The masking technique eliminated irrelevant data and reduced the computational cost of the overall monitoring system. Next, Sobel edge detection combined with Canny edge detection and Hough line transform has been applied for noise reduction. After preprocessing, the blob detection algorithm helped detect the vehicles. Vehicles of varying sizes have been detected by implementing a dynamic thresholding scheme. Detection was done on the first image of every burst. Then, to track vehicles, the model of each vehicle was made to find its matches in the succeeding images using the template matching algorithm. To further improve the tracking accuracy by incorporating motion information, Scale Invariant Feature Transform (SIFT) features have been used to find the best possible match among multiple matches. An accuracy rate of 87% for detection and 80% accuracy for tracking in the A1 Motorway Netherland dataset has been achieved. For the Vehicle Aerial Imaging from Drone (VAID) dataset, an accuracy rate of 86% for detection and 78% accuracy for tracking has been achieved.

Road Traffic Monitoring from Aerial Images Using Template Matching and Invariant Features