Wall Security Dataset(Videos)

Smart Home Automation (SHA) has significantly improved homes’ convenience, comfort, security, and safety. It has gained widespread use due to its intelligent monitoring and quick response capabilities. The current state of SHA enables effective monitoring and motion detection. However, false notifications remain a significant challenge, as they can cause unnecessary alarms in intrusion detection systems. To address this, we propose an intelligent model for a smart home security system that uses computer vision techniques to detect trespasser movement near the boundary wall. We employ a Convolutional Long-Short-Term Memory (ConvLSTM) deep learning model to process a sequence of input video frames captured by a vision sensor (camera) positioned to monitor the boundary wall. The model extracts feature from the frames using convolutional layers and learns temporal dependencies between consecutive frames using LSTM cells. Upon detecting suspicious activity, the system immediately alerts the homeowner. To support this, we developed a large-scale dataset with various environmental conditions and scenarios, such as morning, afternoon, and night, focusing on wall crossing and intrusion detection. The dataset consists of 456 videos, with each class (normal and wall crossing) containing 228 videos. In computer vision, datasets are crucial for object detection. To the best of our knowledge, no publicly available dataset exists for wall crossing and intrusion detection at an early stage. Therefore, we took the initiative to fill this gap. We trained the ConvLSTM model using our developed data set to achieve optimal results. The proposed model is compared with other Convolutional Neural Network (CNN) models highlighted in the results section. The proposed model is discussed with other existing convolutional neural network models, as shown in the paper’s result section. The proposed model achieved 95% validation and 97% test accuracy, significantly surpassing the other pre-trained models.