Lightweight Dynamic Convolutional Network for Crowd Counting Based on Curriculum Reinforcement Learning

In public spaces, high pedestrian concentrations usually lead to congestion and may even pose trampling risks. Upon reaching a specific density threshold, implementing control measures becomes necessary to regulate pedestrian inflows. Therefore, detecting and identifying crowded areas is crucial for pedestrian flow control. Crowd counting is a key technique for achieving this goal. Recently, researchers have dedicated significant efforts to designing convolutional neural networks with various architectures for solving this problem. However, the existing models have structures with high computing power requirements for extreme situations, making it difficult for them to run on edge devices such as surveillance computers. In this paper, we propose a lightweight crowd counting model with a dynamic convolutional kernel for the crowd counting task. The model is built via an encoder–decoderstructure. The encoder extracts high-quality features throughinverted residual layers implemented viaMobileNetV2, which are replaced by a dynamic convolutional kernel. The decoder generates a density map through upsampling and linear layers. A skip connection structure is added to facilitate information exchange between the codecs and reduce the loss of information. Moreover, a training strategy based on curriculum reinforcement learning is presented. This strategy facilitates the integration of samples from diverse datasets,and the difficulty level of each sampling step isdynamically adjusted with a reinforcement learning model. In addition, this strategy can be used to organize the training sequence in each iteration on the basis of sample complexity, thereby achieving enhanced training stability and improved model performance. Comprehensive experimental evidence demonstrates that our model produces superior outcomes to those of competing methods across several benchmark datasets.