Multimodal pedestrian detection using metaheuristics with deep convolutional neural network in crowded scenes

Abstract

Pedestrian detection (PD) is a vital computer vision (CV) problem that is highly employed in several real-time applications, namely autonomous driving methods, robotics, and security observing methods. Simulated by deep learning (DL) approaches to the recognition of generic objects, several investigation mechanisms have attained maximum recognition accuracy for acceptable scale and non-blocked pedestrians. However, the detection efficiency needed to be improved for complex cases like rare pose samples, crowd scenes, and cases with worse visibility due to daytime or weather. Therefore, this study develops a multimodal pedestrian detection system in crowded scenes using metaheuristics and a deep convolutional neural network (MMPD-MDCNN) technique. The MMPD-MDCNN technique’s goal is to identify pedestrians in crowd scenes using different deep-learning models effectively. The proposed MMPD-MDCNN technique integrates three deep learning models: the residual network (ResNet-50), Inception v3, and the capsule network (CapsNet). In addition, the Harris Hawks Optimization (HHO) algorithm is applied for optimal hyperparameter tuning of the deep learning models. For pedestrian detection, the MMPD-MDCNN technique uses the long short-term memory (LSTM) model, and its hyperparameters can be adjusted by the shark smell optimization (SSO) algorithm. To demonstrate the superior performance of the MMPD-MDCNN approach, A comprehensive set of simulations on the INRIA and UCSD datasets was performed to illustrate the superior performance of the MMPD-MDCNN approach. The experimental results suggest that the MMPD-MDCNN model performs well on both datasets.