An Automatic Scale-Adaptive Approach With Attention Mechanism-Based Crowd Spatial Information for Crowd Counting

Abstract

This paper proposes an automatic scale-adaptive approach with attention mechanism-based crowd spatial information addressing the crowd counting task, i.e. a novel cascaded crowd counting network. The proposed network is composed of a classification sub-network to estimate crowd scales and the main network to predict the corresponding density maps. First, the image serves as the input of the classification network and the main network. Second, according to the estimated crowd scale results, the main network structure is adjusted; simultaneously, the feature in the intermediate layer of the classification network is added stepwise into the main crowd counting network. Then, the semantic feature of the classification network is converted into the crowd spatial information mask via the proposed spatial attention conversion module, and the crowd spatial information mask is weighted into the specific feature of the main network. Last, the crowd density map and the crowd counting result are obtained. The experiments on challenging Mall, the Shanghaitech_A and Shanghaitech_B datasets prove the effectiveness, feasibility, and robustness of the proposed method, and the ablation study demonstrates the structure rationality of the proposed network.

A Crowd Counting Framework Combining with Crowd Location

“…Sang et al [21] optimized the geometric adaptive Gaussian kernel function of SaCNN to generate a higher quality real DM. Kong et al [22] proposed an adaptive attention mechanism method to automatically adjust the network structure through the crowd size.…”

“…e performance of the detector in dense scenes is improved, but this scheme is only applicable to video stream data. In dense scenes, due to the severe occlusion, Vora [2] and Kong et al [22] detected the crowd heads, which increased the accuracy of detection. Vora [2] proposed faster R-CNN directly for binary classification tasks, to determine whether the detection frame is a human head and to reduce the number of anchor boxes according to the human head scale, speeding up the detection process.…”

High-Resolution Crowd Density Maps Generation With Multi-Scale Fusion Conditional GAN

“…However, due to heavy occlusions, scale and perspective variations, and irregular cluster, it is extremely difficult to generate accurate density value for each pixel of the input crowd image. Inspired by the successful use of convolution neural network (CNN) in image recognition and image segmentation tasks [ [11]. These methods tend to utilize CNNs with different size of receptive fields to obtain scale aggregation features for adapting the large variation in crowd density.…”