Fig 1.
Flowchart of the proposed approach presenting a step-by-step process of tracking the objects by maintaining the history.
Fig 2.
Identification of static and moving objects inside FoV.
Fig 3.
Estimation of objects using local linear regression.
Table 1.
Object tracking using local linear regression.
Fig 4.
An experiment presenting tracking of a dynamic object in occlusion case.
Fig 5.
Accurate tracking of objects following nonlinear, irregular, parallel and crossing trajectories.
Fig 6.
Validation results in the case of birds flock scenario.
Fig 7.
Human moving in a room (a to l).
Fig 8.
Tracking of a human moving inside a room.
Fig 9.
Boundaries of the approach.
Fig 10.
Application in real-world environment; (a) autonomous driving, (b) people tracking for security purpose.