Hybrid Deep Learning Architecture With K-means Clustering For Weapon Detection In CCTV Surveillance

For quick criminal activity alert, it became quite obvious to use multiple capture points with cameras, and with multiple capture points, there is need for automated criminal activity alerting systems so the human observer can manage all the CCTV feed in real-time, as its humanly not possible go through that many video feed without a crime slipping out undetected. A deep learning based approach for this task on various network have been researched previously. The researchers have tuned with larger and largest network possible to perform weapon detection for surveillance, though they have achieved more than 90% of accuracy for the task but have to pull largest and complex networks possible. Large deep learning models are costly in both computation and memory for a CCTV device to perform AI workload.There was a gap in studying hybrid approach where deep learning along with machine learning based approach are evaluated for the task. To close this gap, our study employs a hybrid approach that combines machine learning and deep learning methods.Training on a customised dataset was attempted initially. But when implementation proved challenging, the study transitioned to implementing the use of the “OD-weapon detection dataset” that had been collected from GitHub. Different levels of accuracy were achieved on first validation by using deep learning models such as VGG16, VGG19, InceptionNet, and MobileNet, which were maximised by applying this diversified collection of weapon images. Techniques for clustering, fine-tuning, and PCA dimension reduction were used to improve the classification performance.