Learning kinematic model of targets in videos from fixed cameras

Object tracking is a key step of video analysis, while a motion model is crucial for object tracking. Concerning videos captured with fixed cameras, a sequence of a target's motion data may suggest the target's kinematic model with respect to the imaging system. In this paper we model the target's kinematic model by learning a long short-term memory network. This kinematic model can serve as a discriminative model and determine the probability of a sequence of velocities. In order to improve the expressive ability of the kinematic model, we partition units of the network into groups and activate groups at different temporal resolutions. With this improvement the kinematic model can also describe the abrupt motion of targets. We have conducted experiments to evaluate the performance of the proposed method, using both a fish tracking method and state-of-the-art tracking methods.