Using Real-Time Online Preprocessed Mouse Tracking for Lower Storage and Transmission Costs

Abstract

Pageview is the most popular webpage analytic metric in all sectors including blogs, business, e-commerce, education, entertainment, research, social media, and technology. To perform deeper analysis, additional methods are required such as mouse tracking, which can help researchers understand online user behavior on a single webpage. However, the geometrical data generated by mouse tracking are extremely large, and qualify as big data. A single swipe on a webpage from left to right can generate a megabyte (MB) of data. Fortunately, the geometrical data of each x and y point of the mouse trail are not always needed. Sometimes, analysts only need the heat map of a certain area or perhaps just a summary of the number of activities that occurred on a webpage. Therefore, recording all geometrical data is sometimes unnecessary. This work introduces preprocessing during real-time and online mouse tracking sessions. The preprocessing that is introduced converts the geometrical data from each x and y point to a region-of-interest concentration, in other words only heat map areas that the analyzer is interested in. Ultimately, the approach used here is able to greatly reduce the storage and transmission cost of real-time online mouse tracking.

Application of Intelligent Analysis Technology of Football Video Based on Online Target Tracking Algorithm of Motion Characteristics in Football Training

“…Li et al [10] directly used the histogram of RGB components to extract the site information, then divided the image to obtain the position of players, used the method of template matching and Kalman filtering to track players, and used histogram back projection to deal with players' occlusion, but the stability of the algorithm is poor. Fajar and Usagawa [11] made use of the images captured by several static cameras placed on the court according to the strategy, generated the observation model based on the data fusion of multicamera coordinates, projected it on the court plane, and generated a multipeak bidirectional probability function, which represents the possible positions of players on the court plane. Liu and Dai [12] put forward that Canny edge extraction is integrated into optical flow field segmentation technology.…”