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.
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. 9. The system according to claim 7 , wherein: the k-means clustering algorithms are applied using identify-sensitive features on all detected objects to select the frames which contain the k centroid objects, where k equals to a total number of objects need to be identified in the video.