Tracking Objects using Artificial Neural Networks and Wireless Connection for Robotics

Tracking objects are used in many areas, and one of them is robotics. The goal in this work focuses on a robot that can follow an object that is in front of it. This application has two links: wireless and Bluetooth. The first one connects a mobile phone mounted on a robot for image acquisition and a personal computer (PC), and the second links a PC and a mobile robot to control the motors by open source, Arduino Board. The algorithm uses several patterns for training the Artificial Neural Network (ANN) and for object identification. Then, it is complemented by the extraction feature in Hue Saturation Value (HSV) color space. This algorithm uses C ++ language with OpenCV libraries for computer vision

Effective Occlusion Handling for Fast Correlation Filter-based Trackers

Correlation filter-based trackers heavily suffer from the problem of multiple peaks in their response maps incurred by occlusions. Moreover, the whole tracking pipeline may break down due to the uncertainties brought by shifting among peaks, which will further lead to the degraded correlation filter model. To alleviate the drift problem caused by occlusions, we propose a novel scheme to choose the specific filter model according to different scenarios. Specifically, an effective measurement function is designed to evaluate the quality of filter response. A sophisticated strategy is employed to judge whether occlusions occur, and then decide how to update the filter models. In addition, we take advantage of both log-polar method and pyramid-like approach to estimate the best scale of the target. We evaluate our proposed approach on VOT2018 challenge and OTB100 dataset, whose experimental result shows that the proposed tracker achieves the promising performance compared against the state-of-the-art trackers