4. The method of claim 3 , wherein said step of combining the maps generated in steps (a)-(c) comprises the steps of: (e) computing a majority semantic class label from the supervised segmentation map for each blob in the unsupervised segmentation map; (f) assigning the majority semantic class label computed in step (e) to all pixels of a blob in the unsupervised segmentation map; (g) computing a majority semantic class label from the moving object detection mask for each blob in the unsupervised segmentation map; (h) assigning the majority semantic class label computed in step (g) to all pixels of a blob in the unsupervised segmentation map; and (i) combining the majority semantic class labels assigned in steps (f) and (h) into a plurality of semantic class labels representing the blobs contained in the at least one frame of video using reasoning based on the semantic meanings of the class labels.
4. The method of claim 3 , wherein said step of combining the maps generated in steps (a)-(c) comprises the steps of: (e) computing a majority semantic class label from the supervised segmentation map for each blob in the unsupervised segmentation map; (f) assigning the majority semantic class label computed in step (e) to all pixels of a blob in the unsupervised segmentation map; (g) computing a majority semantic class label from the moving object detection mask for each blob in the unsupervised segmentation map; (h) assigning the majority semantic class label computed in step (g) to all pixels of a blob in the unsupervised segmentation map; and (i) combining the majority semantic class labels assigned in steps (f) and (h) into a plurality of semantic class labels representing the blobs contained in the at least one frame of video using reasoning based on the semantic meanings of the class labels. 5. The method of claim 4 , wherein said step of combining the majority semantic class labels of step (i) comprises the steps of: (j) modifying the semantic class label from step (f) using the behavior, the behavior being one of moving and stationary, computed from step (h) when the semantic class labels from steps (f) and (h) are consistent; otherwise (k) choosing the semantic class label of step (e) when the confidence of the semantic class label computed is high and the confidence of the moving object detection step (g) is low; otherwise (l) conducting a search for the most likely semantic class label that is consistent with the results of the moving object detection step (g).