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. 2. The method of claim 1 , wherein the processing unit uses the cross-object constraint if the object states {s k t } and {s l t } for views {k} and {l} do not correspond to a same physical object, but instead to an adjacent object in the linear sequence.