CSCNet: A Shallow Single Column Network for Crowd Counting

Zhou, Zhida; Su, Li; Li, Guorong; Yang, Yujuan; Huang, Qingming

doi:10.1109/vcip49819.2020.9301855

Cited by 4 publications

(1 citation statement)

References 16 publications

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“…These single-column networks usually have a deeper network structure, reduce the complexity of the network, and have good counting performance. Zhou et al (2020) proposed a shallower single-column CSCNet, whose fundamental functional structure can capture complementary scale context information and obtain high counting accuracy without deepening the depth of the network. In addition to studying the improvement of the network structures, the design of appropriate loss functions is also an important factor that directly affects the performance of counting models.…”

Section: Related Workmentioning

confidence: 99%

Counting dense object of multiple types based on feature enhancement

Fu,

Min,

Sheng

et al. 2024

Front. Neurorobot.

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IntroductionAccurately counting the number of dense objects in an image, such as pedestrians or vehicles, is a challenging and practical task. The existing density map regression methods based on CNN are mainly used to count a class of dense objects in a single scene. However, in complex traffic scenes, objects such as vehicles and pedestrians usually exist at the same time, and multiple classes of dense objects need to be counted simultaneously.MethodsTo solve the above issues, we propose a new multiple types of dense object counting method based on feature enhancement, which can enhance the features of dense counting objects in complex traffic scenes to realize the classification and regression counting of dense vehicles and people. The counting model consists of the regression subnet and the classification subnet. The regression subnet is primarily used to generate two-channel predicted density maps, mainly including the initial feature layer and the feature enhancement layer, in which the feature enhancement layer can enhance the classification features and regression counting features of dense objects in complex traffic scenes. The classification subnet mainly supervises classifying dense vehicles and people into two feature channels to assist the regression counting task of the regression subnets.ResultsOur method is compared on VisDrone+ datasets, ApolloScape+ datasets, and UAVDT+ datasets. The experimental results show that the method counts two kinds of dense objects simultaneously and outputs a high-quality two-channel predicted density map. The counting performance is better than the state-of-the-art counting network in dense people and vehicle counting.DiscussionIn future work, we will further improve the feature extraction ability of the model in complex traffic scenes to classify and count a variety of dense objects such as cars, pedestrians, and non-motor vehicles.

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Section: Related Workmentioning

confidence: 99%