1. A method for monitoring video data comprising: identifying a feature vector of an event having data corresponding to at least one of a position and a direction of movement of an object in video data; generating an estimated feature vector corresponding to the feature vector using a dictionary that includes a plurality of basis vectors, the generating of the estimated feature vector further comprising: performing a penalized optimization process with the identified feature vector and the plurality of basis vectors in the dictionary to generate a sparse weight vector that corresponds to the identified feature vector, the sparse weight vector including a plurality of elements with each element corresponding to a basis vector in the dictionary; and generating the estimated feature vector from a weighted sum of a plurality of basis vectors in the dictionary that correspond to elements in the sparse weight vector with non-zero weight values; identifying an error between the estimated feature vector and the identified feature vector; identifying a high-interest event in the video data in response to the identified error exceeding a threshold; displaying the video data that includes the high-interest event on a video output device only in response to the identified error exceeding the threshold; receiving a first signal from the video output device indicating the displayed video data do not include a high-interest event; and updating the dictionary in response to receiving the first signal, the updating further comprising: generating a modified sparse weight vector based on the sparse weight vector to set any values that are less than a predetermined threshold from the sparse weight vector to zero; generating another estimated feature vector from another weighted sum of the plurality of basis vectors in the dictionary that correspond to elements in the modified sparse weight vector with non-zero weight values; and generating an additional basis vector based on a difference between the feature vector of the event and the other estimated feature vector; and storing the additional basis vector in the dictionary.