4. The method of claim 3 where said training dictionary is defined as A=[A 1 , A 2 , . . . , A p ] where A i =[A i,1 , A i,2 , . . . , A i,K ]ε n×KT , i=1, 2, . . . , P represents concatenation of sub-dictionaries from all classes belonging to an i-th trajectory, K represents a number of normal event classes, and T represents a number of events per class.
4. The method of claim 3 where said training dictionary is defined as A=[A 1 , A 2 , . . . , A p ] where A i =[A i,1 , A i,2 , . . . , A i,K ]ε n×KT , i=1, 2, . . . , P represents concatenation of sub-dictionaries from all classes belonging to an i-th trajectory, K represents a number of normal event classes, and T represents a number of events per class. 5. The method of claim 4 wherein said joint sparse reconstruction model is given by: Y=AS=[A 1,1 , A 1,2 , . . . , A 1,K , . . . , A i,1 , A i,2 , . . . , A i,K , . . . , A p,1 , A p,2 , . . . , A p,K ][a 1 , . . . , a i , . . . , a p ]Y=AS wherein Y represents a P-object event in the second video input sequence, A represent said training dictionary, and S represent a sparse matrix of coefficient weights determined by solving an optimization problem.