1. A computer-implemented method for video analytics object detection optimization, the method comprising executing on a processing unit the steps of: acquiring video image data over time from a plurality of synchronized cameras having overlapping views of a plurality of objects moving past the cameras and through a scene image in a linear array and with a determined speed; generating for each camera a plurality of object detection states that each have different times of frames of the acquired video image data within a plurality of frames of the camera video stream data, wherein each of the object detection states are associated with a confidence score; selecting ones of the plurality of object detection states for each of the different times that have a highest confidence score optimized by using a global energy function to find maximum unary potentials (ψ(s k t )) of the object detection states as a function of a cross-frame constraint that is defined by other confidence scores of other object detection states from the video data that are acquired by a same one of the cameras at different times from a time of the object detection state, and of a cross-view constraint (T(s k t , s l t ) that is defined by other confidence scores of other object detection states in the video data from another different one of the cameras that has an overlapping field-of-view with the same one camera and that are also acquired at the different times; and defining an optimal state path for a detection of an object from an initial time to a final time of a duration period comprising the selected ones of the plurality of object detection states that have the highest optimized confidence scores; and wherein the unary potentials ψ(s k t ) are determined according to: 
 ψ( s k t )= f ( s k t )Π t≠k T ( s k t ,s l t ); where f(s k t ) is a confidence score of an object state {s k t } returned by an object detector at view {k}; and the processing unit determining the cross-view spatial constraint as a function of the unary potential according to: T ⁡ ( s k t , s l t ) = max ⁡ ( N ⁡ (  s k t - s l t  ; θ kl ) , N ⁡ (  s k t - s l t + ∈  ; θ kl ) ) ; wherein θ kt =[μ v (k, l), Σ v (k,l)] for views {k} and {l}; “μ v ” is a four-by-four matrix of mean values; Σ v ” is a four-by-four covariance matrix; and “ε” is a cross-object spatial constraint that represents an object spacing constant defined by a sequential context of the linear array of the objects determined as a function of spatial attributes of the objects relative to the determined speed of the movement of the cameras relative to the objects.
1. A computer-implemented method for video analytics object detection optimization, the method comprising executing on a processing unit the steps of: acquiring video image data over time from a plurality of synchronized cameras having overlapping views of a plurality of objects moving past the cameras and through a scene image in a linear array and with a determined speed; generating for each camera a plurality of object detection states that each have different times of frames of the acquired video image data within a plurality of frames of the camera video stream data, wherein each of the object detection states are associated with a confidence score; selecting ones of the plurality of object detection states for each of the different times that have a highest confidence score optimized by using a global energy function to find maximum unary potentials (ψ(s k t )) of the object detection states as a function of a cross-frame constraint that is defined by other confidence scores of other object detection states from the video data that are acquired by a same one of the cameras at different times from a time of the object detection state, and of a cross-view constraint (T(s k t , s l t ) that is defined by other confidence scores of other object detection states in the video data from another different one of the cameras that has an overlapping field-of-view with the same one camera and that are also acquired at the different times; and defining an optimal state path for a detection of an object from an initial time to a final time of a duration period comprising the selected ones of the plurality of object detection states that have the highest optimized confidence scores; and wherein the unary potentials ψ(s k t ) are determined according to: 
 ψ( s k t )= f ( s k t )Π t≠k T ( s k t ,s l t ); where f(s k t ) is a confidence score of an object state {s k t } returned by an object detector at view {k}; and the processing unit determining the cross-view spatial constraint as a function of the unary potential according to: T ⁡ ( s k t , s l t ) = max ⁡ ( N ⁡ (  s k t - s l t  ; θ kl ) , N ⁡ (  s k t - s l t + ∈  ; θ kl ) ) ; wherein θ kt =[μ v (k, l), Σ v (k,l)] for views {k} and {l}; “μ v ” is a four-by-four matrix of mean values; Σ v ” is a four-by-four covariance matrix; and “ε” is a cross-object spatial constraint that represents an object spacing constant defined by a sequential context of the linear array of the objects determined as a function of spatial attributes of the objects relative to the determined speed of the movement of the cameras relative to the objects. 6. The method of claim 1 , further comprising: integrating computer-readable program code into a computer system comprising the processing unit, a computer readable memory and a computer readable tangible storage medium; wherein the computer readable program code is embodied on the computer readable tangible storage medium and comprises instructions that, when executed by the processing unit via the computer readable memory, cause the processing unit to perform the steps of acquiring the video image data over time from the synchronized cameras having the overlapping views of the objects moving past the cameras, generating for each camera the plurality of object detection states that are associated with the confidence scores, selecting the ones of the plurality of object detection states for each of the different times that have the highest optimized confidence scores, and defining the optimal state path for the detection of the object from the initial time to the final time of the duration period.