1. A method to track an object in a video, the method comprising: initializing, by one or more processors, a first frame in a video by detecting a search window over an object to be tracked, wherein initializing the first frame comprises defining multiple densely overlapping patches within the search window; populating, by one or more processors, a static dictionary D with the densely overlapping patches from the search window; detecting, by one or more processors, a new frame in the video, wherein the new frame includes the object being tracked; identifying, by one or more processors, candidate patches, in the new frame, that potentially depict the object being tracked; co-locating, by one or more processors, the candidate patches with the multiple densely overlapping patches to form a dynamic candidate dictionary Y of candidate patches; identifying, by one or more processors, candidate patches that best match the densely overlapping patches from the first frame to generate selected candidate patches by minimizing a solution: 
 min∥Dα k −y k ∥ 2 2 +λ∥α k ∥ 1 where the solution minimizes a square of a L2-norm for a distance between atoms in a dictionary D of the densely overlapping patches times an n-dimensional coefficient vector α k (Dα k ) and each candidate patch (y k ) in dictionary Y, plus a Lagrange Multiplier lambda (λ) times an L1-norm of α k , wherein the Lagrange multiplier is determined by the gradient between an initial atom d k from D and the candidate atom y k from Y; weighting, by one or more processors, the selected candidate patches based on a sparse coefficient of confidence of the selected candidate patches belonging to the object being tracked; identifying, by one or more processors, a highest weighted candidate patch, from the selected candidate patches, as a patch that depicts the object being tracked in the new frame of the video; and constructing, by one or more processors, a confidence map for candidate patches in the dictionary Y, wherein the confidence map is a 2-D matrix that depicts a level of confidence that a patch from the new frame matches a densely overlapping patch from the first frame wherein the confidence map is based on: 
 y x,y =α xy D′ [x,y]εO w where α xy is a ‘n’ dimensional coefficient vector for each candidate patch at location (x, y), where [x,y] are elements of (ε) and object window (O w ) that describe locations in an object window in the new frame, wherein D′=[D o D b ], wherein D o is a dictionary of object patches from the object being tracked, wherein D b is a dictionary of background patches outside of the object being tracked, and wherein D o and D b are both used to discriminate the object patches from the background patches.