Automatic Car Counting Method for Unmanned Aerial Vehicle Images

作者：Moranduzzo Thomas^*; Melgani Farid

来源：IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(3): 1635-1647.

DOI：10.1109/TGRS.2013.2253108

• Car detection
• feature extraction
• scale invariant feature transform (SIFT)
• support vector machine (SVM)
• unmanned aerial vehicle (UAV)

This paper presents a solution to solve the car detection and counting problem in images acquired by means of unmanned aerial vehicles (UAVs). UAV images are characterized by a very high spatial resolution (order of few centimeters), and consequently by an extremely high level of details which calls for appropriate automatic analysis methods. The proposed method starts with a screening step of asphalted zones in order to restrict the areas where to detect cars and thus to reduce false alarms. Then, it performs a feature extraction process based on scalar invariant feature transform thanks to which a set of keypoints is identified in the considered image and opportunely described. Successively, it discriminates between keypoints assigned to cars and all the others, by means of a support vector machine classifier. The last step of our method is focused on the grouping of the keypoints belonging to the same car in order to get a "one keypoint-one car" relationship. Finally, the number of cars present in the scene is given by the number of final keypoints identified. The experimental results obtained on a real UAV scene characterized by a spatial resolution of 2 cm show that the proposed method exhibits a promising car counting accuracy.

• 出版日期2014-3