13 Tracking of Moving Objects with Accuracy Guarantees
289
streets, meaning that each segment belongs to precisely one road or street. Each segment identifies its road or street by means of a street code. Chap. 2 offers additional
detail on more comprehensive modeling of road networks.
13.3 Fundamental Tracking Techniques
We proceed to describe three tracking techniques that follow the scenario described
in Sect. 13.2.1 but differ in how they predict the future positions of a moving object.
These were covered by ˇ
Civilis et al. [4]; minor variations of the first and third of these
were also studied by Wolfson and Yin [24] (see Sect. 13.8 for additional discussion).
13.3.1 Point-based Tracking
Using this technique, the server represents an object’s future position as the most
recently reported position. An update is issued by an object when its distance to the
previously reported position deviates from its current GPS position by the specified
threshold. Thus, the movement of an object is represented as a “jumping point.” This
technique is the most primitive among the techniques presented, but it may well be
suitable for movement that is erratic, or undirected, with respect to the threshold
used. An example is the tracking with a threshold of 200 m of children who are
playing soccer.
The algorithm for point-based tracking, PP (Predict with Point), is simple.
Algorithm 13.3.1 PP(mo)
(1) return mo.p
As the prediction is constant, the predicted position is the same as the input position.
Here mo.p is the position of the moving object.
13.3.2 Vector-based Tracking
In vector-based tracking, the future positions of a moving object are given by a linear
function of time, that is, by a start position and a velocity vector. Point-based tracking
then corresponds to the special case where the velocity vector is the zero vector.
A GPS receiver computes both the speed and the heading for the object it is associated with — the velocity vector used in this representation is computed from these
two. Using this technique, the movement of an object is represented as a “jumping
vector.” Vector-based tracking may be useful for the tracking of “directed” movement.
Algorithm PV (Predict with Vector) predicts the location of the given object mo
at a given time t cur .
289
streets, meaning that each segment belongs to precisely one road or street. Each segment identifies its road or street by means of a street code. Chap. 2 offers additional
detail on more comprehensive modeling of road networks.
13.3 Fundamental Tracking Techniques
We proceed to describe three tracking techniques that follow the scenario described
in Sect. 13.2.1 but differ in how they predict the future positions of a moving object.
These were covered by ˇ
Civilis et al. [4]; minor variations of the first and third of these
were also studied by Wolfson and Yin [24] (see Sect. 13.8 for additional discussion).
13.3.1 Point-based Tracking
Using this technique, the server represents an object’s future position as the most
recently reported position. An update is issued by an object when its distance to the
previously reported position deviates from its current GPS position by the specified
threshold. Thus, the movement of an object is represented as a “jumping point.” This
technique is the most primitive among the techniques presented, but it may well be
suitable for movement that is erratic, or undirected, with respect to the threshold
used. An example is the tracking with a threshold of 200 m of children who are
playing soccer.
The algorithm for point-based tracking, PP (Predict with Point), is simple.
Algorithm 13.3.1 PP(mo)
(1) return mo.p
As the prediction is constant, the predicted position is the same as the input position.
Here mo.p is the position of the moving object.
13.3.2 Vector-based Tracking
In vector-based tracking, the future positions of a moving object are given by a linear
function of time, that is, by a start position and a velocity vector. Point-based tracking
then corresponds to the special case where the velocity vector is the zero vector.
A GPS receiver computes both the speed and the heading for the object it is associated with — the velocity vector used in this representation is computed from these
two. Using this technique, the movement of an object is represented as a “jumping
vector.” Vector-based tracking may be useful for the tracking of “directed” movement.
Algorithm PV (Predict with Vector) predicts the location of the given object mo
at a given time t cur .