원문정보
초록
영어
This paper presents a real-time pedestrian detection algorithm for a night environment. By first converting the nighttime image data to the L * a * b * color space, it can be extracted in the area L * with robust noise reduction and contrast adjustment. This data is used to generate pre-data through image subtraction. A background image is generated using the data, and the Cascade Histogram of Oriented Gradient &Kalman filter (CH & K) algorithm is proposed to track the movement of pedestrians. In addition, pre-processing algorithms and the proposed algorithm can replace Histograms of Oriented Gradients (HOGs) with rather heavy computations, and sensitive Haar-like features in the night can be used for real-time pedestrian detection and brightness.
목차
1.Introduction
2. Related Theory
2.1. Lab Color Space
2.2. Cascade
2.3. Hog
3. Proposed Method
3.1. Color Space Conversion
4. Experimental Results
5. Conclusion
Acknowledgment
References