- published: 10 Jul 2015
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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.
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.
Tracking with single target
This video demonstrates single target tracking using a 24 node RF sensor network.
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...
This video demo illustrates the robustness and real-time performance of SIFT-based tracking system with a single USB camera. First, the recognition system finds the target of interest; then the tracking system tracks the SIFT features and locates the target in the scene. Targets missed during the tracking phase can be re-found and re-located by the recognition system.
TrackingPoint is an Austin, Texas-based applied technology company that created the first precision guided firearm (PGF), a long-range rifle system. LEARN HOW TO GET YOUR FEDERAL FIREARMS LICENCE CLICK HERE http://13e4933lo3ir8kba07mdg-6ixz.hop.clickbank.net/ TrackingPoint was formed by CEO John McHale in February 2011. The first PGF prototype was created in March 2011. The company officially launched a publicly available product in January 2013. TrackingPoint's precision guided firearms system uses several component technologies: Networked Tracking Scope: The core engine that tracks the target, calculates range and the ballistic solution, and works in concert with the shooter and guided trigger to release the shot. Barrel Reference System: A fixed reference point that ena...
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 is about Averaging single for Multi-target tracking