Crowd Counting Based on Single-Column Deep Spatiotemporal Convolutional Neural Network

Table 1. Performance indexes of different models on the UCSD data set
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Table 1. Performance indexes of different models on the UCSD data set
Models f_MAE f_MSE
ConvLSTM^[3] 1.30 1.79
Bidirectional ConvLSTM^[3] 1.13 1.43
Ref. [19] 2.24 7.97
Ref. [20] 2.25 7.82
Ref. [10] 1.54 3.02
Ref. [21] 2.07 6.86
Ours 1.05 1.59

Table 2. Performance indexes of different models on the Mall data set
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Table 2. Performance indexes of different models on the Mall data set
Model f_MAE f_MSE
ConvLSTM^[3] 2.24 8.50
Bidirectional ConvLSTM^[3] 2.10 7.60
Ref. [20] 3.59 19.00
Ref.[21] 3.43 17.70
Ours 1.95 7.50

Table 3. Performance indexes of different models on self-built data set
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Table 3. Performance indexes of different models on self-built data set
Model f_MAE f_MSE
ConvLSTM^[3] 4.51 5.91
MCNN^[22] 3.81 4.92
Ours without ST 4.32 5.21
Ours 3.51 5.10

Table 4. Confirmation experiment results1 of different data sets
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Table 4. Confirmation experiment results1 of different data sets
Data set No dilated No ST dilated+No ST
f_MAE f_MSE f_MAE f_MSE
UCSD 1.71 4.25 1.52 4.13
Mall 2.89 9.01 2.13 8.51
Self-built 4.74 6.65 4.32 5.21

Table 5. Confirmation experiment results 2 of different data sets
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Table 5. Confirmation experiment results 2 of different data sets
Data set No dilated No ST No dilated+ ST dilated+ST
f_MAE f_MSE f_MAE f_MSE f_MAE f_MSE
UCSD 1.71 4.25 1.41 3.52 1.05 1.59
Mall 2.89 9.01 2.01 8.43 1.95 7.50
Self-built 4.74 6.65 4.33 5.23 3.51 5.10

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Chunyan Yu, Yan Xu, Lisha Gou, Zhefeng Nan. Crowd Counting Based on Single-Column Deep Spatiotemporal Convolutional Neural Network[J]. Laser & Optoelectronics Progress, 2021, 58(8): 0810011

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