- published: 10 Jul 2015
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Tracking with single target
This video demonstrates single target tracking using a 24 node RF sensor network.
Target tracking with the proposed model for tracking. A 3-gaze template is chosen in order to model the appearance and the shape of the target (i.e., a pedestrian). The learned policy converges to the first gaze, corresponding to the upper part of the human body.
Target tracking with the proposed model for tracking. A 9-gaze template is chosen in order to model the appearance and the shape of the target (i.e., a hockey player). The learned policy converges to the gaze G4. The tracker is not hijacked by the other similar players.
In this simulation, we perform single target tracking. A measurement is received every 11 time steps. With the results shown in the left panel, we have trained the learner over 2000 time steps. The results shown in the right panel do not benefit from learning. Both panels use a gated Kalman filter as the tracker. The current state estimate is shown in red. Measurements at the current time step are shown in green. Measurements from the past 25 time steps are shown in blue.
There is just a single motor connecting the first arm to the base of the pendulum
Final Oral Examination of: Afshin Dehghan For the Degree of: Doctor of Philosophy (Computer Science) Firstly, a new framework for multi-target tracking that uses a novel data association technique employing the Generalized Maximum Clique Problem (GMCP) formulation is presented. The majority of current methods, such as bipartite matching, incorporate a limited temporal locality of the sequence into the data association problem. On the other hand, our approach incorporates both motion and appearance in a global manner. The proposed method incorporates the whole temporal span and solves the data association problem for one object at a time. GMCP is used to solve the optimization problem of our data association. GMCP leads us to a more accurate approach to multi-object tracking; however, it...
A single vehicle is tracked by an airborne camera. The target is identified by its color distribution, and a bounding box surrounds the estimated position of the target in the image. The corners of the bounding box are used as feature points in the tracking algorithm, which regulates the sample mean and variance of the points in the image to keep the target in view.
This video demonstrates two target tracking using a 24 node RF sensor network.