Turkish Journal of Electrical Engineering and Computer Sciences
This paper focuses on vehicle detection based on motion features in driving videos. Long-term motion information can assist in driving scenarios since driving is a complicated and dynamic process. The proposed method is a deep learning based model which processes motion frame image. This image merges both spatial (frame) and temporal (motion) information. Hence, the model jointly detects vehicles and their motion from a single image. The trained model on Toyota Motor Europe Motorway Dataset reaches 83% mean average precision (mAP). Our experiments demonstrate that the proposed method has a higher mAP than a tracking-based model. The proposed method runs real-time in driving videos which enables the model to be used in time-critical applications such as autonomous driving and advanced driving assistance systems.
Vehicle-motion detection, driving video, object detection, motion profile, spatial-temporal images
KILIÇARSLAN, MEHMET and TEMEL, TANSU
"Motion-aware vehicle detection in driving videos,"
Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 30:
1, Article 5.
Available at: https://journals.tubitak.gov.tr/elektrik/vol30/iss1/5
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