Deep intelligent technique for person Re-identification system in surveillance images

Abstract

Person re-identification is the system that aims to attain the re-identity of a particular person captured by different surveillance cameras. However, it is still a challenging problem in the surveillance system. The more considerable variation of light conditions, body poses, angles illumination, and occlusion makes it difficult for the system to re-identify the persons. Recently, the study has been significantly improved by the use of deep intelligence frameworks. However, it faces some limitations, such as insufficient features and poor accuracy. Therefore, a novel Horned Lizard Googlenet Forecasting System (HLGFS) is developed in this research to better result in person re-identification. The novelty of the research lies in integrating Horned Lizard optimization with GoogleNet for fine-tuned and efficient forecasting to re-identify the person. Initially, the surveillance images were preprocessed to filter the low-level noise features. Further, the relevant features were extracted based on the Horned Lizard optimization function. Subsequently, by analyzing the extracted features, the re-identity of the person is identified and received by matching and ranking. Moreover, the similarity percentage of the query and identified images was measured through structure similarity. The process of the designed model is tested using the CUHK03, Market1501, and DukeMTMC re-id dataset in the Python platform. Finally, the forecasting efficiency of the approach is validated and related to existing techniques. The accuracy of HLGFS is 97.8 %, and the mAP is 97.6 % for the CUHK03 dataset, with 97.68 % accuracy, and 98.87 % mAP for the Market1501 dataset and for the DukeMTMC re-id dataset, the model achieved 96.65 % accuracy and 96.65 % mAP.