Exploration of Normalized Cross Correlation to Track the Object through Various Template Updating Techniques

Object tracking is a process devoted to locate the pathway of moving object in the succession of frames. The tracking of the object has been emerged as a challenging facet in the fields of robot navigation, military, traffic monitoring and video surveillance etc. In the first phase of contributions, the tracking of object is exercised by means of matching between the template and exhaustive image through the Normalized Cross Correlation (NCCR). In order to update the template, the moving objects are detected using frame difference technique at regular interval of frames. Subsequently, NCCR or Principal Component Analysis (PCA) or Histogram Regression Line (HRL) of the template and moving objects are estimated to find the best match to update the template. The second phase discusses the tracking of object between the template and partitioned image through the NCCR with reduced computational aspects. However, the updating schemes remain same. Here, an exploration with varied bench mark dataset has been carried out. Further, the comparative analysis of the proposed systems with different updating schemes such as NCCR, PCA and HRL has been succeeded. The offered systems considerably reveal the capability to track an object indisputably under diverse illumination conditions.