Open AccessA new pedestrian recognition system based on edge detection and different census transform features under weather conditions
Author(s) -
Mohammed Razzok,
Abdelmajid Badri,
Ilham El Mourabit,
Yassine Ruichek,
Aïcha Sahel
Publication year - 2022
Publication title -
iaes international journal of artificial intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.341
H-Index - 7
eISSN - 2252-8938
pISSN - 2089-4872
DOI - 10.11591/ijai.v11.i2.pp582-592
Subject(s) - computer science , artificial intelligence , histogram , discriminative model , pattern recognition (psychology) , gabor filter , support vector machine , classifier (uml) , histogram of oriented gradients , benchmark (surveying) , pedestrian detection , computer vision , extreme learning machine , pedestrian , feature extraction , image (mathematics) , geography , artificial neural network , cartography , archaeology
Pedestrian detection has so far achieved great success in normal illumination, while pedestrians captured in extreme weather are often ignored. This paper investigates the importance of studying the effects of weather conditions on the recognition task, such as blurring and low contrast. Many image restoration techniques have recently been proposed, but are still insufficient to remove weather effects from images. We present our strong new pedestrian recognition system against climate situations, which is based on locating contours cues by applying multiple edge filters and extracting multiple features from images such as census transform (CT), modified census transform (MCT), and local gradient pattern (LGP) without performing any image restoration algorithm. The next stage involves finding the most discriminative characteristics using feature selection (FS) techniques. Finally, we use the final feature vector as an input to a radial basis function-based support vector machine classifier (RbfSVM) for pedestrian recognition. Experiments are performed on the daimler pedestrian classification benchmark dataset. Results show that the area under the curve (AUC) and the detection rate of our model are less affected by weather conditions compared to other common models like histogram of oriented gradients (HOG) and gabor filter bank (GFB) detectors.