2. The processor-implemented method of claim 1 , wherein updating the neural network includes at least one of creating the first cluster representing the first object type or updating the first cluster in the plurality of clusters representing the first object type based on the micro-feature vector.
1. A processor-implemented method for tracking an object in a video scene, the method comprising: determining, via at least one processor, 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; determining, via the at least one processor, kinematics of an object in the video scene based at least in part on the at least one pixel-level characteristic; generating, via the at least one processor, 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; updating, via the at least one processor, 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 representing an object type; classifying, via the at least one processor, 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 tracking, via the at least one processor, the object in the video scene based on the micro-feature vector and the first object type.