Improved Crowd Counting Method Based on Scale-Adaptive Convolutional Neural Network

Abstract

Crowd counting is a challenging task due to the influence of various factors, such as scene transformation, complex crowd distribution, uneven illumination, and occlusion. To overcome such problems, scale-adaptive convolutional neural network (SaCNN) used a convolutional neural network to obtain high-quality crowd density map estimation and integrate the density map to get the estimated headcount. To obtain better performance on crowd counting, an improved crowd counting method based on SaCNN was proposed in this paper. The spread parameter, i.e., the standard variance, of geometry-adaptive Gaussian kernel used in SaCNN was optimized to generate a higher quality ground truth density map for training. The absolute count loss with weight 4e-5 was used to jointly optimize with the density map loss to improve the network generalization ability for crowd scenes with few pedestrians. Also, a random cropping method was applied to improve the diversity of training samples to enhance network generalization ability. The experimental results upon ShanghaiTech public dataset showed that the proposed method can obtain more accurate and more robust results on crowd counting than those of SaCNN. INDEX TERMS Crowd counting, convolutional neural network, scale-adaptive convolutional neural network (SaCNN), density map, scale-adaptive, absolute count loss.

Estimation of the Number of Passengers in a Bus Using Deep Learning

“…After defining the test dataset, to verify the effectiveness of the proposed algorithm in passenger number estimation, we compare it to two methods: SaCNN [25] and MCNN [26]. To account for the performances of the different detection models, we used the mean absolute error (MAE) and root mean squared error (RMSE), as shown in Equations (7) and (8), to evaluate the effectiveness of models based on the references [28][29][30], respectively. N img is the number of test images, x i g is the actual number of passengers in the test image, andx i p is the number of passengers on the bus estimated by the different methods.…”

“…However, the existing density labeling method is mostly marked according to the method mentioned in [20], so the semantic structure information is added to improve the accuracy of the data labeling, and the estimation of the number of crowd scenes can be more effective. The authors in [29] improved the scale-adaptive CNN (SaCNN) proposed in [25]. Adaptive Gaussian kernels are used to estimate the parameter settings for different head sizes.…”

DeepCorn: A Semi-Supervised Deep Learning Method for High-Throughput Image-Based Corn Kernel Counting and Yield Estimation

“…Our proposed network uses VGG-16 (Simonyan and Zisserman, 2014) as a backbone for feature extraction. Originally proposed for image classification, the VGG-16 network stacks convolutional layers with a fixed kernel size of 3 3, which usually generalizes well to other vision tasks including object counting and detection (Shi et al, 2018; Boominathan et al, 2016; Gao et al, 2020b; Sang et al, 2019; Valloli and Mehta, 2019; Liu et al, 2016; Kumar et al, 2019). We exclude the last max-pooling layer and all fully connected from the VGG network.…”

HAGN: Hierarchical Attention Guided Network for Crowd Counting

“…Zhou et al [1] proposed a new kind of network structure with deformable convolution for crowd counting tasks. Sang et al [2] proposed a new model by improving the Scale-adaptive CNN (SaCNN) architecture with a backbone of fixed small receptive fields [43]. Cheng et al [3] proposed a new kind of learning strategy named Multi-column Convolutional Neural Network (McML) for multi-column networks, which could effectively solve the multi-scale learning problem of the network, and has the advantages of less parameter and be less prone to overfitting.…”

“…Crowd counting is the basis of crowd analysis and scene understanding [1], [2]. However, getting accurate crowd numbers in realistic application scenarios is a challenging task due to scale variations of the crowd head.…”