Mosaicking based optimal threshold image enhancement for violence detection with deep quadratic attention mechanism

Violence is a prevalent societal issue that poses significant threats to individuals and communities. To address this challenge, researchers have developed various machine learning models to detect and prevent violent incidents. The violence detection problem statement is a critical challenge that requires innovative and collaborative solutions. By leveraging advancements in artificial intelligence, machine learning, and data analytics, systems are developed to effectively detect violence, prevent its occurrence, and protect vulnerable individuals and communities. Therefore proposed framework utilizes Differential Evolution Algorithm (DEA) along with mosaicking process which helps in enhancing the quality of the image in order to detect the presence of violence. Hockey fight dataset consist of 40 video clips out of which 20 are labelled as fight videos and remaining 20 is labelled as non-fight videos. The image is split as 30:20 ratio and the image will undergo either brightness enhancement or contrast enhancement according to the threshold value obtained by the fitness function. In mosaicking process, Ruled KNN based SURF algorithm is employed in the proposed model as it works faster and helps in providing high accuracy which assist in detecting the violence present in the images. The classification technique employed in the proposed method is Deep Quadratic Attention Mechanism (DQAM). It helps in providing better results for image classification, reducing the convergence rate and helps in improving the normalization. The performance of the proposed method is evaluated using different performance metrics which includes PSNR, SSIM and proposed method is compared with previous phase model in comparative analysis for comparing the efficiency of the proposed and existing model.