Region-Aware Quantum Network for Crowd Counting

Crowd counting has substantial practical applications in various consumer-oriented areas, particularly for safety assessments and marketing strategies. However, considering the complexities of the capturing conditions, the unavoidable background interference possesses the potential to disrupt the effectiveness of established counting methods, and it further poses degraded counting performance. To address this challenge, we propose a Region-Aware Quantum Network (RAQNet) by attentively learning from the crowd region. It consists of four key components, namely the feature extractor, the object region awareness module (ORA), the quantum-driven calibration (QDC) module, and the decoder module. The cascaded ORA modules are engineered for the extraction of local information, which addresses background interference. Additionally, two QDC modules are incorporated to capture global information, which utilizes quantum states to calibrate features. Extensive experimental results conducted on four crowd benchmark datasets and three cross-domain datasets prove that the RAQNet outperforms the state-of-the-art competitors, both subjectively and objectively.