Results and Conclusions

Model- Convolution Neural Network

The use of convolutional neural networks has become increasingly prevalent in image classification tasks, particularly in the field of computer vision. By applying filters to an image, convolutional neural networks can extract relevant features that are essential for identifying the target object or class. In this case, the CNNs have been used to extract critical features that distinguish between accident and non-accident images. By training the network on a large dataset of labeled images, the model can learn to differentiate between the two classes with high accuracy, making it a valuable tool for surveillance and monitoring systems. 

The base model used is MobileNetV2, which is a pre-trained deep learning model that has been trained on the ImageNet dataset. MobileNetV2 is a popular convolutional neural network architecture that is designed to be computationally efficient and suitable for deployment on mobile and embedded devices. 

The final model is defined using a Sequential model, which sequentially stacks the layers on top of the base model. The layers include three Conv2D layers, each with a kernel size of 3 and ReLU activation function. These convolutional layers extract features from the input images. The Flatten layer is used to convert the 2D feature maps output by the convolutional layers into a 1D vector, which is then passed to a fully connected Dense layer with a softmax activation function, which outputs the probabilities of the input image belonging to each class in class_names.

Model Summary

Sample Schematic Representation of the Model

Training the Model

When we train a machine learning model, we provide it with some data to learn from. During the training process, the model learns to identify patterns and relationships in the data that allow it to make accurate predictions on new, unseen data. The process of training involves running the data through the model multiple times, with each pass known as an epoch. The number of epochs is a hyperparameter that we set before training the model, and it determines how many times the model will be trained on the entire dataset. In this case, the model has been trained on the training dataset for 10 epochs. After each epoch, the model's performance is evaluated on the validation dataset to see how well it is learning. The model's weights and biases are updated after each epoch based on the feedback received during the validation step, allowing the model to gradually improve its performance. By the end of the training process, the model will have learned to make accurate predictions on the training data and hopefully generalize well to new, unseen data.

Evaluating the performance of the Model

The model has an accuracy of 0.959184 and a validation loss of 0.143275 during the validation process. During the testing process, the model has an accuracy of 0.91 and a loss of 0.264198. This suggests that the model performs well on the validation set and is also able to generalize to new, unseen data. However, there is a slight decrease in accuracy and an increase in loss in the testing phase, which could indicate some overfitting on the training data. Further analysis of the model's performance on different data sets and hyperparameter tuning may be required to optimize its performance.

Confusion Matrix

Analyzing the Accuracy and Loss

The plot of the accuracy and loss over epochs is a common way to visualize the training progress of a machine learning model. In this case, the orange line represents the accuracy, which shows the percentage of correctly classified instances, while the blue line represents the loss, which measures how far off the predicted class probabilities are from the actual class probabilities. 

On the training set the accuracy rises to over 90% and the loss drops nicely until epoch 10 is a good sign that the model is learning and improving its ability to classify accident and non-accident instances. However, it's important to note that the model's performance on the test set should also be considered, as overfitting can occur if the model is too closely tuned to the training set. In this case, the test accuracy of 91% suggests that the model is performing well on new, unseen data. 

On the validation set, the accuracy rises to around 95% while the loss drops below 0.2 for epoch 10. It's great to see that the model's performance is improving on the validation set, with an accuracy of around 95% and a loss below 0.2 after 10 epochs. This indicates that the model is able to generalize well on new, unseen data, and is not overfitting to the training data. However, it's worth noting that the test accuracy is slightly lower than the validation accuracy at around 91%. This could be due to the differences in the data distribution between the validation and test sets, or simply due to random variations in the data.

Visualizing the Model Output 

Conclusions

Using a convolutional neural network to identify accidents using images extracted from real-time CCTV footage is a promising approach. The model built using a MobileNetV2 base and several convolutional layers was able to achieve a high accuracy of 91% on the test set, indicating its effectiveness in identifying accidents. The high accuracy on the validation set of 95.9% further confirms the model's ability to generalize well to new data.

One potential takeaway from this approach is that pre-trained models like MobileNetV2 can provide a good starting point for building more specialized models. In this case, the MobileNetV2 base was able to capture important features of the input images, which were then refined by the additional convolutional layers. Another takeaway is that image classification with deep learning models can have important real-world applications, such as identifying accidents in CCTV footage. This approach can potentially improve the efficiency of emergency response systems and reduce response times, leading to better outcomes for those involved in accidents. 

Future Work

Using the CNN model to identify accidents from real-time surveillance video cam footage could be a promising application of this approach. By deploying the model on surveillance cameras, the model can process live footage and instantly classify it as an accident or non-accident instance. The model can then notify the response team to take quick action, potentially reducing the response time and improving the chances of saving lives. However, there are some challenges that need to be addressed in future work. 

One challenge could be the accuracy and reliability of the model when deployed in real-world scenarios. The model should be tested thoroughly under various conditions, such as different lighting, weather, and traffic conditions, to ensure that it is robust enough to handle the variability of real-world data. Another challenge could be privacy concerns, as the use of surveillance cameras and the deployment of AI models in public spaces raise ethical and legal questions. Appropriate measures should be taken to ensure that the model is used ethically and in compliance with data protection and privacy regulations. Overall, the application of the CNN model to real-time surveillance video cam footage has significant potential to improve the safety and security of public spaces and should be further explored in future research.