Learning Multi-Level Density Maps for Crowd Counting

By Xiaoheng Jiang, Li Zhang, Pei Lv, Yibo Guo, Ruijie Zhu, Yafei Li, Yanwei Pang, Xi Li, Bing Zhou, Mingliang Xu. 2020

59 citations recorded by the source corpus.

People in crowd scenes often exhibit the characteristic of imbalanced distribution. On the one hand, people size varies largely due to the camera perspective. People far away from the camera look smaller and are likely to occlude each other, whereas people near to the camera look larger and are relatively sparse. On the other hand, the number of people also varies greatly in the same or different scenes. This article aims to develop a novel model that can accurately estimate the crowd count from a given scene with imbalanced people distribution. To this end, we have proposed an effective multi-level convolutional neural network (MLCNN) architecture that first adaptively learns multi-level density maps and then fuses them to predict the final output. Density map of each level focuses on dealing with people of certain sizes. As a result, the fusion of multi-level density maps is able to tackle the large variation in people size. In addition, we introduce a new loss function named balanced loss (BL) to impose relatively BL feedback during training, which helps further improve the performance of the proposed network. Furthermore, we introduce a new data set including 1111 images with a total of 49 061 head annotations. MLCNN is easy to train with only one end-to-end training stage. Experimental results demonstrate that our MLCNN achieves state-of-the-art performance. In particular, our MLCNN reaches a mean absolute error (MAE) of 242.4 on the UCF_CC_50 data set, which is 37.2 lower than the second-best result.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Ying Shan
2. Pei Lv
3. Qi Tian
4. Fei Wu
5. Yanwei Pang
6. Yongjian Fu
7. Tianzhu Zhang
8. Xi Li

In Computer Vision and Pattern Recognition

1. Ying Shan
2. Xi Li
3. Tianzhu Zhang
4. Qi Tian
5. Yanwei Pang
6. Fei Wu
7. Pei Lv
8. Yongjian Fu

Citations in the corpus

• MgSvF: Multi-Grained Slow versus Fast Framework for Few-Shot Class-Incremental Learning (2020, background): “Rehearsal strategies [15], [16], [17], [54], [55], [56], [57], [58], [59], [60], [61], [62] replay old tasks information when learning the new task, and the past knowledge is memorized by storing old tasks’ exemplars or old tasks data distribution via generative models.”
• LayoutDiffusion: Controllable Diffusion Model for Layout-to-Image Generation (2023, background): “This is mainly due to the ambiguity of the text and its weakness in precisely expressing the position of the image space [6, 15, 22, 43–45].”
• Attention Scaling for Crowd Counting (2020, methodology): “Recent years have witnessed progressive improvement in crowd counting from traditional methods [7, 10, 26, 39, 2] to CNN based methods [1, 24, 13, 14, 23, 29, 25].”