ST-CNN: Spatial-Temporal Convolutional Neural Network for crowd counting in videos

Miao, Y. and Han, J. and Gao, Y. and Zhang, B. (2019) ST-CNN: Spatial-Temporal Convolutional Neural Network for crowd counting in videos. Pattern Recognition Letters, 125. pp. 113-118. ISSN 0167-8655

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The task of crowd counting and density maps estimating from videos is challenging due to severe occlusions, scene perspective distortions and diverse crowd distributions. Conventional crowd counting methods via deep learning technique process each video frame independently with no consideration of the intrinsic temporal correlation among neighboring frames, thus making the performance lower than the required level of real-world applications. To overcome this shortcoming, a new end-to-end deep architecture named Spatial-Temporal Convolutional Neural Network (ST-CNN) is proposed, which unifies 2D convolutional neural network (C2D) and 3D convolutional neural network (C3D) to learn spatial-temporal features in the same framework. On top of that, a merging scheme is performed on the resulting density maps, taking advantages of the spatial-temporal information simultaneously for the crowd counting task. Experimental results on two benchmark data sets â Mall dataset and WorldExpo′10 dataset show that our ST-CNN outperforms the state-of-the-art models in terms of mean absolutely error (MAE) and mean squared error (MSE).

Item Type:
Journal Article
Journal or Publication Title:
Pattern Recognition Letters
Uncontrolled Keywords:
?? crowd analysiscrowd countingspatio-temporal featureconvolutiondeep learningmean square errorconvolutional neural networklearning techniquesperspective distortionspatial-temporal featuresspatio temporal featurestemporal correlationsneural networksartificia ??
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Deposited On:
14 Feb 2020 13:35
Last Modified:
15 Jul 2024 19:22