A novel framework for motion segmentation and tracking by clustering incomplete trajectories

In this paper, we present a framework for visual object tracking based on clustering trajectories of image key points extracted from an image sequence. The main contribution of our method is that the trajectories are automatically extracted from the image sequence and they are provided directly to a model-based clustering approach. In most other methodologies, the latter constitutes a difficult part since the resulting feature trajectories have a short duration, as the key points disappear and reappear due to occlusion, illumination, viewpoint changes and noise. We present here a sparse, translation invariant regression mixture model for clustering trajectories of variable length. The overall scheme is converted into a maximum a posteriori approach, where the Expectation–Maximization (EM) algorithm is used for estimating the model parameters. The proposed method detects the different objects in the input image sequence by assigning each trajectory to a cluster, and simultaneously provides their motion. Numerical results demonstrate the ability of the proposed method to offer more accurate and robust solutions in comparison with other tracking approaches, such as the mean shift tracker, the camshift tracker and the Kalman filter.

► Multiple target tracking and motion segmentation by clustering trajectories.
► Trajectories have variable length, can be translated in space and may be incomplete.
► Clustering is achieved through a sparse regression mixture model.
► Model parameters are estimated by the EM algorithm.