Crowd counting via joint SASNet and a guided batch normalization network

Official URL: http://dx.doi.org/10.1109/SIU59756.2023.10223901

Recent studies on crowd counting have achieved promising results by using convolutional neural network (CNN) architectures. However, due to the large variation in scene distribution in real-world crowd datasets, it remains a challenge to achieve high performance using standard CNN methods. Such methods often suffer from performance drops. To address this challenge, this paper proposes a new crowd-counting approach that combines three state-of-the-art methods: Guided-Batch-Normalization, which adapts the model using unseen dataset normalization parameters; the Scale Adaptive Selection Network, which uses a multi-level network to obtain variation feature representations; and Distribution-Matching-Count, which uses a new loss function between prediction and ground truth maps. Combining these methods results in improved performance. Extensive experiments across multiple datasets have demonstrated that the proposed approach outperforms state-of-the-art methods.