Turkish Journal of Electrical Engineering and Computer Sciences
Abstract
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.
DOI
10.3906/elk-2101-93
Keywords
Vehicle-motion detection, driving video, object detection, motion profile, spatial-temporal images
First Page
63
Last Page
78
Recommended Citation
KILIÇARSLAN, M, & TEMEL, T (2022). Motion-aware vehicle detection in driving videos. Turkish Journal of Electrical Engineering and Computer Sciences 30 (1): 63-78. https://doi.org/10.3906/elk-2101-93
Included in
Computer Engineering Commons, Computer Sciences Commons, Electrical and Computer Engineering Commons
