Research Article
Moving Vehicle Detection and Classification Using Gaussian Mixture Model and Ensemble Deep Learning Technique
Table 2
Performance analysis of the proposed ensemble deep learning technique on the BIT Vehicle Dataset in terms of precision, recall, and accuracy.
| Feature extraction | Classifier | Precision (%) | Recall (%) | Accuracy (%) |
| SPT | MSVM | 64.90 | 78 | 75 | KNN | 69 | 60.83 | 72 | DNN | 70.45 | 72 | 62.43 | LSTM | 73.97 | 80.80 | 79.60 | Ensemble | 78.91 | 86.82 | 90 | WLD | MSVM | 70 | 75 | 80 | KNN | 70.02 | 79.97 | 81 | DNN | 78.20 | 86.55 | 81.02 | LSTM | 79 | 84.60 | 83.20 | Ensemble | 81.02 | 84.39 | 86.22 | Hybrid (SPT + WLD) | MSVM | 82.94 | 92.19 | 93 | KNN | 87 | 92 | 94.94 | DNN | 92.28 | 97.20 | 96.66 | LSTM | 93.90 | 96.97 | 98.98 | Ensemble | 98.24 | 99.72 | 99.28 |
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