Object Detection In Surveillance Video

Abstract

Surveillance is a process to gather an information from the analysis. The surveillance can be applied in all applications like video, communication, data monitoring and in hospital. In all these field, the surveillance is performed to monitor the abnormal conditions in it. In this, the ship detection is performed in the marine surveillance videos. Because, the ships are easily crossed the other country border without knowing or with any intentional. In such a state, there is a risk of safety. To avoid this, the marine officers will routine check their area to find out any invasion of ships. It is possible during normal weather conditions. But, in abnormal conditions, it becomes a difficult process and time consuming is high for monitoring. In order to overcome such difficulties, the image processing technique were used to determine the ships in the sea. Most of the techniques were based on the YOLO processing which requires perfect threshold for detection. In order to eliminate the thresholding process, in this an optimized regional convolutional neural network is proposed. Here, the frames in the video will be subjected to firefly optimization based clustering process to separate the shore and sea area. Then, the sea area is subjected to regional convolutional neural network for different type of ship detection. Due to this optimized approach, it able to detect the ship effectively and in minimal time consumption of regional convolutional neural network by applying on the sea area alone. The ship detection process is implemented using MATLAB R 2018a version and evaluated in terms of accuracy, precision and Recall. The proposed optimized RCNN able to outperform saliency based YOLO function for ship detection.