A Novel Approach For Detection And Tracking Of Vessels On Maritime Sequences

Object detection, classification and tracking are prime components in all computer vision application. The research problem here is to detect, classify and track small objects (such as ships, boats etc.) on maritime scenario. Main purpose of object detection in maritime is to secure the country from various rocket launchers, sea side firearms. According to today's scenario, security is very important in maritime applications. This paper showcases experiment results for object detection using Speeded Up Robust Features (SURF), Binary Robust Invariant Scalable Key points (BRISK), Lukas-kanade on standard dataset IPATCH PETS 2016. However, the state of the art methods limits in handle camouflage scenario. This paper proposes an adaptive Lucas-kanade approach that handles such scenarios. The proposed approach utilizes interaction of arithmetic mean and histogram equalization with optical flow (Lucas-Kanade) approach to resolve camouflage. Finally, the proposed approach is evaluated using standard parameters such as recall, precision and f1 score. The performance measures depict that the proposed approach outperforms state of the art trackers.