Automatic Estimation of Multiple Motion Fields using Object Trajectories and Optical Flow

Multiple motion fields are an efficient way of summarising the movement of objects in a scene and allow an automatic classification of objects activities in the scene. However, their estimation relies on some kind of supervised learning e.g., using manually edited trajectories. This paper proposes an automatic method for the estimation of multiple motion fields. The proposed algorithm detects multiple moving objects and their velocities in a video sequence using optical flow. This leads to a sequence of centroids and corresponding velocity vectors. A matching algorithm is then applied to group the centroids into trajectories, each of them describing the movement of an object in the scene. The paper shows that motion fields can be reliably estimated from the detected trajectories leading to a fully automatic procedure for the estimation of multiple motion fields.

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