Efficient vehicle detection and tracking strategy in aerial videos by employing morphological operations and feature points motion analysis

• Published in: Multimedia Tools and Applications Vol. 79; no. 35-36; pp. 26023 - 26043
• Main Authors: Gomaa, Ahmed, Abdelwahab, Moataz M., Abo-Zahhad, Mohammed
• Format: Journal Article
• Language: English; Japanese
• Published: New York Springer Science and Business Media LLC 01.09.2020; Springer US; Springer Nature B.V
• Subjects: Algorithms; Cameras; Cluster analysis; Clustering; Computer Communication Networks; Computer Science; Data Structures and Information Theory; Morphology; Motion perception; Multimedia Information Systems; Occlusion; Special Purpose and Application-Based Systems; Tracking; Vector quantization; Aerial surveillance; Vehicle detection and tracking; KLT tracker; Remote sensing; K-means clustering; Morphological operations
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-020-09242-5

Real-time automatic detection and tracking of moving vehicles in videos acquired by airborne cameras is a challenging problem due to vehicle occlusion, camera movement, and high computational cost. This paper presents an efficient and robust real-time approach for automatic vehicle detection and tracking in aerial videos that employ both detections and tracking features to enhance the final decision. The use of Top-hat and Bottom-hat transformation aided by the morphological operation in the detection phase has been adopted. After detection, background regions are eliminated by motion feature points’ analysis of the obtained object regions using a combined technique between KLT tracker and K-means clustering. Obtained object features are clustered into separate objects based on their motion characteristic. Finally, an efficient connecting algorithm is introduced to assign the vehicle labels with their corresponding cluster trajectories. The proposed method was tested on videos taken in different scenarios. The experimental results showed that the recall, precision, and tracking accuracy of the proposed method were about 95.1 %, 97.5%, and 95.2%, respectively. The method also achieves a fast processing speed. Thus, the proposed approach has superior overall performance compared to newly published approaches.