Convolutional neural network based object detection system for video surveillance application

Video surveillance is emerging as a promising solution for the humans to lead a peaceful and independent life in their homes. The recognition and localization of moving objects plays a central role in the video surveillance. The manual surveillance is time consuming and tedious. Therefore, novel object detection via optimized deep learning model is developed in this work that supports the video surveillance application. In the initial phase, proposed angle and distance based Local Binary Pattern (LBP) features are extracted. Subsequently, these extracted features are subjected to object detection phase, where optimized Convolutional Neural Network (CNN) will expose the information about the detected object. Further, the learning quality of CNN is decided by the weight parameter, which is responsible to distinguish the objects with high accuracy. Therefore, a hybrid optimization concept referred as Sealion Leader Update with Particles (SLUP) is introduced in this research work to fine-tune the weight of CNN. Finally, a comparative analysis is made between the proposed and the extant approaches in terms of "positive, negative, and other measures."