Abstract
Crowd counting, the task of estimating the number of individuals in a crowded scene, has gained increasing attention in computer vision research. However, crowd counting remains a challenging problem due to the complex and diverse nature of crowd scenes. In recent years, domain adaptation has emerged as a promising approach to improve crowd counting performance by adapting a pre-trained model to a target domain with different characteristics. This paper provides a survey of domain adaptation-based crowd counting algorithms, including their methods, datasets, and evaluation metrics. Overall, domain adaptation shows great potential in improving the accuracy and robustness of crowd counting algorithms, and further research in this direction is expected to lead to more effective and practical crowd counting solutions.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: 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.
