1. A method for detecting multi-object anomalies in transportation related video footage, said method comprising: receiving in an offline training phase a first input video sequence at a first traffic location and identifying at least one normal event involving P moving objects, where P is greater than 1; assigning in said offline training phase said at least one normal event in said first input video sequence to at least one normal event class and building a training dictionary suitable for joint sparse reconstruction; receiving in an online detection phase a second input video sequence captured at a second traffic location similar to said first traffic location and identifying at least one event involving P moving objects; reconstructing in said online detection phase an approximation of said event within second input video sequence with respect to said training dictionary using a joint sparse reconstruction model; and determining in said online detection phase whether said event within second input video sequence is anomalous by evaluating an outlier rejection measure of said approximation and comparing said measure against a predetermined threshold, wherein said outlier rejection measure is given by JSCI ⁡ ( S ′ ) = K · max i ⁢  λ i ⁡ ( S ′ )  row , 0 /  S ′  row , 0 - 1 K - 1 , ⁢ where S ′ = [ α 1 , 1 α 2 , 1 α 1 , 2 α 2 , 2 ] and α i,j are coefficient sub-vectors corresponding to coefficient vectors α i , where i=1, 2,. .. , P represents concatenation of sub-dictionaries from all classes belonging to an i-th trajectory and j represents a given class, K represents a number of normal event classes, λ i (S′) represents a characteristic function whose only non-zero entries are the rows in S′ that are associated with the i-th class, and row norm ∥ ∥ row,0 represents the number of non-zero rows of a matrix.