Purpose–The conventional pedestrian detection algorithms lack in scale sensitivity.The purpose of this paper is to propose a novel algorithm of self-adaptive scale pedestrian detection,based on deep residual network(...Purpose–The conventional pedestrian detection algorithms lack in scale sensitivity.The purpose of this paper is to propose a novel algorithm of self-adaptive scale pedestrian detection,based on deep residual network(DRN),to address such lacks.Design/methodology/approach–First,the“Edge boxes”algorithm is introduced to extract region of interestsfrompedestrian images.Then,the extracted boundingboxesare incorporatedto differentDRNs,one is a large-scale DRN and the other one is the small-scale DRN.The height of the bounding boxes is used to classify the results of pedestrians and to regress the bounding boxes to the entity of the pedestrian.At last,a weighted self-adaptive scale function,which combines the large-scale results and small-scale results,is designed for the final pedestrian detection.Findings–Tovalidatetheeffectivenessandfeasibilityoftheproposedalgorithm,somecomparisonexperiments have been done on the common pedestrian detection data sets:Caltech,INRIA,ETH and KITTI.Experimental resultsshowthattheproposedalgorithmisadaptedforthevariousscalesofthepedestrians.Fortheharddetected small-scale pedestrians,the proposed algorithm has improved the accuracy and robustness of detections.Originality/value–By applying different models to deal with different scales of pedestrians,the proposed algorithm with the weighted calculation function has improved the accuracy and robustness for different scales of pedestrians.展开更多
文摘Purpose–The conventional pedestrian detection algorithms lack in scale sensitivity.The purpose of this paper is to propose a novel algorithm of self-adaptive scale pedestrian detection,based on deep residual network(DRN),to address such lacks.Design/methodology/approach–First,the“Edge boxes”algorithm is introduced to extract region of interestsfrompedestrian images.Then,the extracted boundingboxesare incorporatedto differentDRNs,one is a large-scale DRN and the other one is the small-scale DRN.The height of the bounding boxes is used to classify the results of pedestrians and to regress the bounding boxes to the entity of the pedestrian.At last,a weighted self-adaptive scale function,which combines the large-scale results and small-scale results,is designed for the final pedestrian detection.Findings–Tovalidatetheeffectivenessandfeasibilityoftheproposedalgorithm,somecomparisonexperiments have been done on the common pedestrian detection data sets:Caltech,INRIA,ETH and KITTI.Experimental resultsshowthattheproposedalgorithmisadaptedforthevariousscalesofthepedestrians.Fortheharddetected small-scale pedestrians,the proposed algorithm has improved the accuracy and robustness of detections.Originality/value–By applying different models to deal with different scales of pedestrians,the proposed algorithm with the weighted calculation function has improved the accuracy and robustness for different scales of pedestrians.
