14. The system of claim 11 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.
14. The system of claim 11 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. 15. The system of claim 14 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 represents said training dictionary, and S represents a sparse matrix of coefficient weights determined by solving an optimization problem.