Crowd density estimation is an important part of intelligent crowd monitoring. However, there are still many problems in density estimation due to the complexity of crowd scenes. Aiming at the high-density scenes with varied scales, we present a method based on cross-adversarial loss and global feature for crowd counting, so as to achieve the purpose of capturing more feature details and reducing the impact of background noise more effectively. First, we use the cross-adversarial loss to generate the residual map, which makes use of the consistency between different scales and solves the homogenization problem of fused density map. Then, we extract large-range context information and focus on key information in global spatial features for the generation of a residual map. Finally, the high-resolution density map is used to estimate the crowd counting. Experiments on three datasets confirm that the proposed method has good adaptability in scenes with obvious distribution change, not just in extracting high-quality features for density map estimation but also for accurate crowd counting.
In most sparse representation methods for face recognition (FR), occlusion problems were usually solved via removing the occlusion part of both query samples and training samples to perform the recognition process. This practice ignores the global feature of facial image and may lead to unsatisfactory results due to the limitation of local features. Considering the aforementioned drawback, we propose a method called varying occlusion detection and iterative recovery for FR. The main contributions of our method are as follows: (1) to detect an accurate occlusion area of facial images, an image processing and intersection-based clustering combination method is used for occlusion FR; (2) according to an accurate occlusion map, the new integrated facial images are recovered iteratively and put into a recognition process; and (3) the effectiveness on recognition accuracy of our method is verified by comparing it with three typical occlusion map detection methods. Experiments show that the proposed method has a highly accurate detection and recovery performance and that it outperforms several similar state-of-the-art methods against partial contiguous occlusion.
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