Improving the Learning of Multi-column Convolutional Neural Network for Crowd Counting

Abstract

Tremendous variation in the scale of people/head size is a critical problem for crowd counting. To improve the scale invariance of feature representation, recent works extensively employ Convolutional Neural Networks with multi-column structures to handle different scales and resolutions. However, due to the substantial redundant parameters in columns, existing multi-column networks invariably exhibit almost the same scale features in different columns, which severely affects counting accuracy and leads to overfitting. In this paper, we attack this problem by proposing a novel Multicolumn Mutual Learning (McML) strategy. It has two main innovations: 1) A statistical network is incorporated into the multi-column framework to estimate the mutual information between columns, which can approximately indicate the scale correlation between features from different columns. By minimizing the mutual information, each column is guided to learn features with different image scales. 2) We devise a mutual learning scheme that can alternately optimize each column while keeping the other columns fixed on each mini-batch training data. With such asynchronous parameter update process, each column is inclined to learn different feature representation from others, which can efficiently reduce the parameter redundancy and improve generalization ability. More remarkably, McML can be applied to all existing multi-column networks and is end-to-end trainable. Extensive experiments on four challenging benchmarks show that McML can significantly improve the original multi-column networks and outperform the other state-of-the-art approaches.

An Enhanced Scale Robust Network for Crowd Counting

“…The proposed method is compared with the state-of-the-art approaches, including CG-DRCN [33], ADCrowdNet [34], DSSINet [36], Cheng et al [39], L2SM [35], etc. Table 1 shows the results of ShanghaiTech dataset.…”

HAGN: Hierarchical Attention Guided Network for Crowd Counting

“…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. Sindagi and Patel [5] proposed advanced counting methods which consists of multi-level and multi-directional information fusion from multi-layer networks.…”

Efficient Crowd Counting via Structured Knowledge Transfer

“…Recently, deep neural networks [4,6,10,32,35,41,44,58,68,71,72,76] have become mainstream in the task of crowd counting and have made remarkable progress. To acquire better performance, most of the state-of-the-art methods [13,28,31,36,40,62,66] utilized heavy backbone networks (such as the VGG model [56]) to extract features.…”