T $$^2$$ 2 CNN: a novel method for crowd counting via two-task convolutional neural network

This paper investigates the issue of crowd counting for crowd images. A novel method named two-task convolutional neural network (T $$^2$$ CNN) is proposed to simultaneously learn two tasks for crowd counting: the tasks of dense degree classification and density map estimation. Basically, T $$^2$$ CNN contains two main modules: dense degree classification (DDC) and density map estimation. Generally, it is sufficient for crowd counting to estimate density maps of images. It is known that images may have different dense degrees and the local regions in the same image have different dense degrees. Thus, dense degree may provide the high-level prior of images and is excepted to help the estimation of density maps. To better estimate density maps of images, T $$^2$$ CNN incorporates the DDC module into the task of density map estimation. T $$^2$$ CNN takes the scale-adaptive convolutional neural network as the estimator of density maps, which adopts multi-scale layers to tackle the scale and perspective variations that exist in crowd images commonly. To verify the effectiveness and robustness of our model, we conduct extensive experiments on the Shanghaitech, UCF_CC_50 and WorldExpo’10 datasets. Experimental results indicate that T $$^2$$ CNN achieves good performance.