Abstract
The monitoring of tourist behaviors, coupled with the recognition of scenic spots, greatly improves the quality and safety of travel. The visual information is the underlying features of scenic spot images, but the semantics of the information have not been satisfactorily classified or described. Based on image processing technologies, this paper presents a novel method for scenic spot retrieval and tourist behavior recognition. Firstly, the framework of scenic spot image retrieval was constructed, followed by a detailed introduction to the extraction of scale invariant feature transform (SIFT) features. The SIFT feature extraction includes five steps: scale space construction, local space extreme point detection, precise positioning of key points, determination of key point size and direction, and generation of SIFT descriptor. Next, multiple correlated images were mined for the target scenic spot image, and the feature matching method between the target image and the set of scenic spot images was introduced in details. On this basis, a tourist behavior recognition method was designed based on temporal and spatial consistency. The proposed method was proved effective through experiments. The research results provide theoretical reference for image retrieval and behavior recognition in many other fields.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.