14. A non-transitory computer-readable storage medium storing computer-executable instructions for tracking an object in a video scene, the computer-executable instructions comprising instructions to: determine at least one pixel-level characteristic for a foreground patch in a first video frame from a plurality of video frames of a video scene, the foreground patch including a set of pixels containing a portion of scene foreground in the first video frame of the video scene; determine kinematics of an object in the video scene based at least in part on that at least one pixel-level characteristic; generate a micro-feature vector based at least in part on the kinematics of the object, the micro-feature vector corresponding to at least one micro-feature value; update a neural network based at least in part on the micro-feature vector, the neural network including a plurality of clusters, each cluster in the plurality of clusters representative of an object type; classify the object in the first video frame of the video scene as a first object type based on the updated neural network, the first object type being represented by a first cluster in the plurality of clusters; and track the object in the video scene based on the micro-feature vector and the first object type.
14. A non-transitory computer-readable storage medium storing computer-executable instructions for tracking an object in a video scene, the computer-executable instructions comprising instructions to: determine at least one pixel-level characteristic for a foreground patch in a first video frame from a plurality of video frames of a video scene, the foreground patch including a set of pixels containing a portion of scene foreground in the first video frame of the video scene; determine kinematics of an object in the video scene based at least in part on that at least one pixel-level characteristic; generate a micro-feature vector based at least in part on the kinematics of the object, the micro-feature vector corresponding to at least one micro-feature value; update a neural network based at least in part on the micro-feature vector, the neural network including a plurality of clusters, each cluster in the plurality of clusters representative of an object type; classify the object in the first video frame of the video scene as a first object type based on the updated neural network, the first object type being represented by a first cluster in the plurality of clusters; and track the object in the video scene based on the micro-feature vector and the first object type. 20. The non-transitory computer-storage medium of claim 14 , wherein the computer-executable instructions to generate the micro-feature vector include instructions to evaluate values representing the kinematics of the object.