Supervised evolutionary learning: Use of gradient histogram & particle swarm algorithm to detection & tracking pedestrian in sequence of infrared images

Abstract

Recently, tracking and pedestrian detection from various images have become one of the major issues in the field of image processing and statistical identification. In this regard, using evolutionary learning-based approaches to improve performance in different contexts can greatly influence the appropriate response. There are problems with pedestrian tracking/identification, such as low accuracy for detection, high processing time, and uncertainty in response to answers. Researchers are looking for new processing models that can accurately monitor one's position on the move. In this study, a hybrid algorithm for the automatic detection of pedestrian position is presented. It is worth noting that this method, contrary to the analysis of visible images, examines pedestrians' thermal and infrared components while walking and combines a neural network with maximum learning capability, wavelet kernel (Wavelet transform), and particle swarm optimization (PSO) to find parameters of learner model. Gradient histograms have a high effect on extracting features in infrared images. As well, the neural network algorithm can achieve its goal (pedestrian detection and tracking) by maximizing learning. The proposed method, despite the possibility of maximum learning, has a high speed in education, and results of various data sets in this field have been analyzed. The result indicates a negligible error in observing the infrared sequence of pedestrian movements, and it is suggested to use neural networks because of their precision and trying to boost the selection of their hyperparameters based on evolutionary algorithms.