ABSTRACT:

Mean-shift tracking plays an important role in computer vision applications because of its robustness, ease of implementation and computational efficiency. In this study, a fully automatic multiple-object tracker based on mean-shift algorithm is presented. Foreground is extracted using a mixture of Gaussian followed by shadow and noise removal to initialise the object trackers and also used as a kernel mask to make the system more efficient by decreasing the search area and the number of iterations to converge for the new location of the object. By using foreground detection, new objects entering to the field of view and objects that are leaving the scene could be detected. Trackers are automatically refreshed to solve the potential problems that may occur because of the changes in objects' size, shape, to handle occlusion-split between the tracked objects and to detect newly emerging objects as well as objects that leave the scene. Using a shadow removal method increases the tracking accuracy. As a result, a method that remedies problems of mean-shift tracking and presents an easy to implement, robust and efficient tracking method that can be used for automated static camera video surveillance applications is proposed. Additionally, it is shown that the proposed method is superior to the standard mean-shift.


Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here


PROJECT TITLE : Partial Computation Offloading and Adaptive Task Scheduling for 5G-enabled Vehicular Networks ABSTRACT: In order to pique the interest of prospective users in the emerging 5G-enabled vehicular networks, a wide
PROJECT TITLE : Deep Visual Odometry with Adaptive Memory ABSTRACT: A novel deep visual odometry (VO) method that takes into account global information by selecting memory and refining poses is presented here. The currently available
PROJECT TITLE : Data Dissemination in VANETs Using Clustering and Probabilistic Forwarding Based on Adaptive Jumping Multi-Objective Firefly Optimization ABSTRACT: The dissemination of data within a VANETs network calls for
PROJECT TITLE : Adaptive Hierarchical Attention-Enhanced Gated Network Integrating Reviews for Item Recommendation ABSTRACT: There have been a number of very successful studies that have focused on integrating ratings and reviews
PROJECT TITLE : Context-aware and Adaptive QoS Prediction for Mobile Edge Computing Services ABSTRACT: Mobile edge computing (MEC) has recently gained a significant amount of momentum due to the fact that it permits the utilization

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry