7. An object identification system, comprising: an object detection module configured to detect target-class object occurrences and related-class object occurrences in each video shot of an input video; a hint information generation module configured to generate hint information including a small subset of frames representing the input video; an object tracking and recognition module configured to perform object tracking and recognition based on the hint information and to combine tracking and recognition results; and an output module configured to output labeled objects based on the combined tracking and recognition results; wherein the hint information generation module is further configured to: apply a video summarization approach; apply k-means clustering algorithms using identify-sensitive features on all detected objects; and perform initial object identification using a knowledge database wherein the hint information possesses a desired identity coverage, a desired local representation, a desired pose variation coverage and a desired illumination variation coverage.
10. The system according to claim 7 , wherein: provided that i, p, q are integers; L is a feature vector that is sensitive to object identity; Sim(·) is a function that measures similarity, the local representation of the ith frame is define by: A ⁡ ( i ) = { ∑ q = 1 X i ⁢ ⁢ ∑ p = 1 X i - 1 ⁢ ⁢ ( Sim ⁡ ( L i - 1 , p , L i , q ) ) X i ⁢ X i - 1 if ⁢ ⁢ i = N 1 2 ⁢ ∑ q = 1 X i ⁢ ⁢ ∑ p = 1 X i - 1 ⁢ ⁢ ( Sim ⁡ ( L i - 1 , p , L i , q ) ) X i ⁢ X i - 1 + 1 2 ⁢ ∑ q = 1 X i ⁢ ⁢ ∑ p = 1 X i + 1 ⁢ ⁢ ( Sim ⁡ ( L i + 1 , p , L i , q ) ) X i ⁢ X i + 1 otherwise where N is a total number of frames in the input video which contain detection results; L i,q is a feature vector of object O q in the ith frame; L i−1,p is a feature vector of object O p in the (i−1)th frame; L i+1,p is a feature vector of object O p in the (i+1)th frame; and X i−1 , X i and X i+1 are a total number of objects on the ith-1 frame, the ith frame and the ith+1 frame, respectively.