DecideNet: Counting Varying Density Crowds Through Attention Guided Detection and Density Estimation

By Jiang Liu, Chenqiang Gao, Deyu Meng, Alexander G. Hauptmann. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2017

379 citations recorded by the source corpus.

In real-world crowd counting applications, the crowd densities vary greatly in spatial and temporal domains. A detection based counting method will estimate crowds accurately in low density scenes, while its reliability in congested areas is downgraded. A regression based approach, on the other hand, captures the general density information in crowded regions. Without knowing the location of each person, it tends to overestimate the count in low density areas. Thus, exclusively using either one of them is not sufficient to handle all kinds of scenes with varying densities. To address this issue, a novel end-to-end crowd counting framework, named DecideNet (DEteCtIon and Density Estimation Network) is proposed. It can adaptively decide the appropriate counting mode for different locations on the image based on its real density conditions. DecideNet starts with estimating the crowd density by generating detection and regression based density maps separately. To capture inevitable variation in densities, it incorporates an attention module, meant to adaptively assess the reliability of the two types of estimations. The final crowd counts are obtained with the guidance of the attention module to adopt suitable estimations from the two kinds of density maps. Experimental results show that our method achieves state-of-the-art performance on three challenging crowd counting datasets.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/9c3fd2815932a97f6813ed51c575b2f0cb394854

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Citations in the corpus

Crowd Counting and Density Estimation by Trellis Encoder-Decoder Networks (2019, methodology): “Notably, most of these methods follow a patch-based counting mechanism [52, 44, 27, 1, 37, 21, 11, 2], where the full density map is obtained by concatenating discrete density patches.”
NWPU-Crowd: A Large-Scale Benchmark for Crowd Counting and Localization (2020, neutral): “Reg+Det Net [35]: a subnet of DecideNet.”
Bayesian Loss for Crowd Count Estimation With Point Supervision (2019, methodology): “[24] proposed Deci-deNet to adaptively decide whether to use a detection model or a density map estimation model.”
Learning Multi-Level Density Maps for Crowd Counting (2020, methodology): “DecideNet of Liu et al. [49] combines both detection and density estimation techniques to together accomplish the counting task.”
Distribution Matching for Crowd Counting (2020, background): “Most recent works [20, 35, 15, 4, 31, 50, 27, 40, 29, 26, 54, 8, 25, 55, 61, 59, 17, 48, 21, 23, 30, 47, 53, 43, 37, 56, 18, 39, 24, 44, 42] are based on density map estimation, which has been shown to be more robust than detection-then-count and count regression approaches.”
Learning From Synthetic Data for Crowd Counting in the Wild (2019, result): “In addition, we find the results on Shanghai Tech B achieve a good level, even outperforms some early supervised methods [43, 35, 31, 36, 20].”
Context-Aware Crowd Counting (2018, methodology): “As in previous work [41, 42, 31, 30, 19, 5, 21, 36, 32, 28, 33] on this dataset, we report the MAE of each scene, as well as the average over all scenes.”
MirrorGAN: Learning Text-To-Image Generation by Redescription (2019, background): “Attention models have been extensively exploited in computer vision and natural language processing, for instance in object detection [21, 6, 18, 41], image/video captioning [34, 9, 31], visual question answering [2, 33, 36, 22], and neural machine translation [19, 8].”