| Summary: | 碩士 === 國立中央大學 === 資訊工程學系 === 105 === There are many mature pedestrian detection methods that had been developed so far. The widespread popularity of driving support system and the emerging of unmanned vehicles let pedestrian detection possesses more practical value and wider application space. Due to the arising of deep learning recently, there is a trend by incorporating deep learning into pedestrian detection. However, deep learning requires high-level hardware and tremendous amount of computation no matter in learning or detection to hinder the practicality of pedestrian detection. In this thesis, a pedestrian detection system is designed for vehicle images without using concurrent computation which can run under general hardware.
In our work, the ROIs (Region of Interest) are firstly predicted based on the camera status of video to reduce unnecessary feature calculation and target search. Then, the Fast Feature Pyramids algorithm is employed to calculate features to further reduce the time spent in the feature calculation phase. Finally, Cascade DPM (Deformable Part Models) method is utilized to detect pedestrians. The speed of our proposed system can uplift the speed to 2.54 times faster than Cascade DPM with slightly lowering precision and recall rate.
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