Vehicle Detection in UAV Videos Using CNN-SVM
| Autor: | Najiya Koderi Valappil, Qurban A. Memon |
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| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | Advances in Intelligent Systems and Computing ISBN: 9783030736880 SoCPaR |
| DOI: | 10.1007/978-3-030-73689-7_22 |
| Popis: | Conventional monitoring devices are usually kept at fixed locations which yields a fixed surveillance coverage. Unmanned aerial vehicles (UAVs) are receiving much attention from researchers in traffic monitoring due to their low cost, high flexibility, and wide view. Unlike stationary surveillance, the camera platform of UAVs is in constant motion and makes it difficult to process for data extraction. The inaccuracy in detection rates of vehicles from UAV videos becomes the motivation for combining optical flow methods with supervised learning algorithms. The proposed method incorporates steps that make use of the Kanade–Lucas optical flow method for moving object detection, connected graphs theory and CNN-SVM for further classification. Optical flow generated contains some background objects detected as vehicle when the camera platforms are moving. The classifier rules out the presence of any other moving objects to be detected as vehicles. The proposed method is tested on few stationary and moving aerial videos. The system is found to be 100% accurate in case of stationary aerial videos and 98% accurate in moving videos. |
| Databáze: | OpenAIRE |
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