A Survey of Crowd Counting Algorithm Based on Domain Adaptation

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.

An In-Depth Analysis of Domain Adaptation in Computer and Robotic Vision

“…[175] Presents a model that works upon generative adversarial networks for semi-supervised learning Can improve performance by learning from both labeled and unlabeled data [176] Introduces a new method for learning invariant features for domain adaptation Can improve performance by learning representations that are invariant to domain shift [177] Introduces a new method for domain adaptation that combines invariant representations and self-supervised learning Can improve performance by learning representations that are invariant to domain shift and using self-supervised learning to learn features that are transferable to new domains [178] Presents a model that works upon meta-learning for transferable features Can improve performance by learning features that are transferable to new domains [179] Presents a model that works upon multi-task learning and attention Can improve performance by using multi-task learning and attention to learn representations that are invariant to domain shift [180] Presents a model that works upon multi-task learning for few-shot image classification Can improve performance by learning multiple tasks with few examples [181] Presents a model that works upon patch-level self-supervised learning Can improve performance by using patch-level self-supervised learning to learn features that are invariant to domain shift [182] Presents a model that works upon self-supervised contrastive learning Can improve performance by learning representations that are invariant to domain shift [183] Presents a model that works upon self-supervised contrastive learning Can improve performance by learning representations that are invariant to domain shift [184] Presents a model that works upon self-supervised learning and synthetic data Can improve performance by using self-supervised learning to learn features that are invariant to domain shift and by generating synthetic data that are like the target domain [185] Presents a model that works upon synthetic data and domain-invariant feature aggregation…”