1. A method for tracking at least one target subject in a camera network, the method comprising the steps of: receiving, at a user interface, a selection of at least one target subject in a first image taken by a first camera from a user; extracting, by a feature module, one or more features of the selected at least one target subject; storing, by a feature database, the extracted one or more features of the selected at least one target subject; correlating, by a correlation module, the extracted one or more features of the selected at least one target subject, among a set of at least two cameras; applying, by the correlation module, a first equation which quantifies a probabilistic likelihood another target subject found in a second camera is the same as the target subject found in the first camera, wherein the probabilistic likelihood is defined in terms of L as: 
 L (Target Subject in First Camera→Target Subject in Second Camera)= F First Camera (Target Subject in First Camera){circle around (×)} F Second Camera (Target Subject in Second Camera)); where {circle around (×)} is a correlation between the Target Subject in First Camera and Target Subject in Second Camera; applying, by a feature weight module, a second equation which takes a linear combination of the correlated one or more extracted features to quantify a ranking while matching the other target subject found in the second camera and the target subject found in the first camera, and modifying criticality of a plurality of features among the extracted one or more features over another plurality of features among the extracted one or more features, wherein the ranking is described in terms of E as: 
 E=Sum i→n ( w c i w f i F i ); where E is the rank measure, Sum i is the linear summation of a feature, n is the number of correlation models, w c i is the correlation weight of the correlation using a feature i, w f i is the feature weight of the feature i for the current searched target subject, and Fi is the feature correlation model between the two cameras using a feature i; ranking, by a ranking module, a plurality of potential matches for the first target based on the second equation, wherein a higher E value for a potential match among the plurality of potential matches is indicative of a higher rank based on parameters configured in the second equation; automatically re-ranking, by the ranking module, the plurality of potential matches by reconfiguring the parameters in the first equation and the second equation; iteratively performing, by the ranking module, one or more search processes with a new input in order to enhance a camera-to-camera correlation based on the one or more extracted features; responsive to determining the selected at least one target subject is present in one or more images from a set of cameras, applying, by a predicting adjustment module, a first analytics solution, wherein the first analytics solution predicts an occurrence of the selected at least one target in a next camera based on finding the at least one selected target in a previous camera; responsive to determining the selected at least one target subject is present in the one or more images from the set of cameras, applying, by the correlation refinement module, a second analytics solution, wherein the second analytics solution populates a first plurality of queries based on one or more correlation models; and responsive to determining the selected at least one target subject is present in the one or more images from the set of cameras, applying, by the feature weight module, a third analytics solution, wherein the third analytics solution populates a second plurality of queries based on weighted criticality of the one or more extracted features.