Cross-Modal Information Aggregation and Distribution Method for Crowd Counting

Crowd counting is a fundamental and challenging task in computer vision. However, existing methods are relatively limited in dealing with scale and illumination changes simultaneously. To improve the accuracy of crowd counting and address the challenges of illumination and scale changes, we adopt the concept of crowding degree information. Due to the fact that a count map can accurately obtain the population in an image and solve the occlusion problem, we use the count map as a specific form of crowding degree information and propose a new cross-modal information aggregation and distribution model for crowd counting. We first input the crowding degree information into LibraNet and modify it with Information Aggregation Transfer (IAT) and Information Distribution Transfer (IDT) modules to obtain a count map. Then, light information, thermal information and crowding degree information are respectively input into the network through RGB image, themal image, and count map. A more accurate density map can be obtained through multiple convolution operations and IADM processing to improve counting accuracy. Finally, the density map is integrated to obtain the number of people. Experiments demonstrate that our methods provide superior quality and higher parallelism. Therefore, we can obtain higher-accuracy density maps by using light information, thermal information, and crowding degree information.