Video Based Tracking & Recognition via (Wireless) Network Transmission

Automated object tracking and recognition in live video have become an important research topic as well as application in the image processing and computer vision community in recent years. Among them, the Continuously Adaptive Menu Shift Algorithm (CamShift) is an adaptation of the Mean Shift algorithm for object tracking which is intended as a step towards head and face tracking for a perceptual user interface. To study the algorithm of CamShift and implement the algorithm to realize object tracking will help us to further study its effectiveness as a general-purpose object tracking approach in the case where no assumptions have been made about the target to be tracked. Transmitting the video with tracking results is so meaningful in practice that we decide to integrate them together. Network Transmission involves topics such as improving the compression rate, increasing the transmission speed, and reliable video coding/decoding and transferring. The suite of widely used protocols Transmission Control Protocol/Internet Protocol (TCP/IP) has become the standard for network communication in recent years. This is due in large part to the explosive growth of the Internet, and the need for diverse platforms, devices, and operating systems to share data in a "language" everyone understands. We will study related issues and implement our proposed methods in the project. Other issues such as friendly user interface design and implementation, system integration and maintenance, and etc., are equally important for our synthesized system— a video based tracking & recognition via (wireless) network transmission system. The main goal of our research focuses on how to carry out recognition and tacking of a moving object in time based on simple background and how to provide reliable and speedy video transmission over the network. We are going to choose a suitable tracking algorithm based on the features of our object and study how to implement the algorithm, increase the efficiency as well as improve the accuracy on the proposed algorithms and methods. We will study how to achieve a high accuracy that 100% object and gesture recognition and every object in the scene will be detected and tracked as well. For our wireless network model, we will try to improve the compression ratio to be 200, its resolution to be 352×288, and its transferring speed to be 10Kbps-1.0Mbps. In addition, we use the TCP protocol to transfer video on the LAN/Internet to ensure reliable transferring. Finally, we will design a friendly user …