An Improved Pedestrian Detection Method Based on Adaboost and Performance Analysis
Pedestrian detection, which has a wide application in surveillance, advanced robotics, and especially intelligent vehicles, is an important area in computer vision. This paper applies a detection approach based on improved Adaboost algorithm. We use a dataset to train the weak classifiers (with different numbers) to cascade to be strong classifiers, in which we employ optimized strategy of sample weight adjustment to reduce the over-fit. After constructing a strong classifier, we apply different scale of sliding widow to shift and calculate the corresponding features to classify them as pedestrians or non-pedestrians. The experiments show that different numbers of weak classifiers layer and different scale of sliding windows can give different performance in detecting.
Y. X. Zhang and X. D. Gao, "An Improved Pedestrian Detection Method Based on Adaboost and Performance Analysis", Advanced Materials Research, Vols. 317-319, pp. 877-880, 2011