A Stochastic Statistical Approach for Tracking Human Activity

Modern research for tracking Human-Object by means of various statistical and mathematical shows a new method enriching the conventional methods. The use covariance considering the images in matrix form for collecting features is a proven approach for tracking object/image in preference to the usual histogram based object/image depiction replica frequently used in well-liked methodology. In this paper we propose few robust statistical approaches for tracking an object/image with the help of mathematical model. The improved mathematical model followed by covariance-chaser is capable to prove its superiority with an eye to generate a prominent algorithm leading to generate improved object/image tracking accurateness by decreasing the completing time. These mathematical models are evaluated to a covariance-chaser chaser and the popular histogram-supportive tracker method. With the help of publicly available dataset a huge quantitative assessment is done pinpointing the effectiveness of the obtainable model. Our model is capable to achieve momentous speeding in human-object tracking dynamically in the better way and is capable to decrease the error for false-tracking compared to the earlier histogram-based and other approaches. It is proved that the accuracy rate based on mathematical-detection-model (MDM) is approximately 94.3% as compared to the conventional model with 89.1 percent.