Object Tracking with Improved Detector of Objects Similar to Target
I didn't read the paper but let me provide some intuition about object detection and tracking. When you try to track a target in a video, object detection algorithms might not be enough and you need to support the algorithm with target tracking methods in bayesian framework. It could be Kalman filter or particle filter. 
In particle filter or any Bayesian filter you first define your state which is composed of positions, velocities and dimensions in your case. You create random samples of states using importance distribution and use them to estimate the density of your state. 
You are kind of correct by saying that these samples are points of pixels in the frame. However, there exist more information in the samples. There are also velocities and dimensions inside those samples. The algorithm eliminate less possible samples and move forward with more possible ones. In this way, you obtain most probable pixels that object could exist. You also obtain most probable velocity and dimension for the object.