| NNs | Neural Networks |
| CNNs | Convolutional NNs |
| RNNs | Recurrent NNs |
| FCL | Fully Connected Layer |
| UAV | Unmanned Aerial Vehicle |
| ReLU | Rectified Linear Unit |
| GTD | Ground Truth Density |
| ED | Estimated Density |
| GLCM | Gray Level Co-Occurrence Metrics |
| HOG | Histogram Oriented Gradient |
| LBP | Local Binary Pattern |
| KLT | Kanade–Lucas–Tomasi |
| GANs | Generative Adversarial Networks |
| MAE | Mean Absolute Error |
| MSE | Mean Square Error |
| STA | ShanghaiTech-A (a dataset) |
| STB | ShanghaiTech-B (a dataset) |
| WE | World Expo 10 (a dataset) |
| CNN-CC | CNN Crowd Counting |
| Network-CNN-CC | Network-based CNN-CC techniques |
| Basic-CNN-CC | Basic CNN-CC techniques |
| Context-CNN-CC | Context-aware CNN-CC techniques |
| Scale-CNN-CC | Scale-aware CNN-CC techniques |
| Multi-task-CNN-CC | Multitask CNN-CC techniques |
| Image-view-CNN-CC | Image-view-based CNN-CC techniques |
| Aerial-view-CNN-CC | Aerial-view-based CNN-CC techniques |
| Perspective-CNN-CC | Perspective-view-based CNN-CC techniques |
| Patch-based-CNN-CC | Patch-based CNN-CC techniques |
| Whole-image-CNN-CC | Whole-image-based CNN-CC techniques |
| Training-CNN-CC | Training-approach-based CNN-CC techniques |