Chaser Priori Wolf (CPW) Optimization an Improved Optimization Technique Video Content Classification and Detection

Abstract

Optimizers play a crucial role in video object detection by promoting the training and improving the performance of the model. Optimizers are responsible for minimizing the loss function during training. The parameters of models are updated iteratively based on the gradients of the loss parameters. By continuously adjusting the parameters in the direction of the steepest descent, optimizers guide the model towards convergence, reducing the loss and improving the object detection performance. In the proposed paper hybrid optimizer named chaser priori wolf optimizer is proposed. The chaser priori wolf optimization is based on the hybridization of cat swarm optimization and coyote optimization. Well-known optimizers like SGD, ADAM, adagrad, adadelta and RMSprop are used as default optimizers by researchers. The proposed work introduced CPW optimizer which works for classification to improve the convergence and feature selection. The comparative result showed an increase in the performance of CNN based YOLO model. The results are compared concerning sensitivity, specificity and accuracy. Results clearly showed improvement in all performance metrics and the average improvement in comparison with state of art architecture is 10.3%.