Vector field analysis for multi-object behavior modeling

This paper proposes an end-to-end system to recognize multi-person behaviors in video, unifying different tasks like segmentation, modeling and recognition within a single optical flow based motion analysis framework. We show how optical flow can be used for analyzing activities of individual actors, as opposed to dense crowds, which is what the existing literature has concentrated on mostly. The algorithm consists of two steps - identification of motion patterns and modeling of motion patterns. Activities are analyzed using the underlying motion patterns which are formed by the optical flow field over a period of time. Streaklines are used to capture these motion patterns via integration of the flow field. To recognize the regions of interest, we utilize the Helmholtz decomposition to compute the divergence potential. The extrema or critical points of this potential indicates regions of high activity in the video, which are then represented as motion patterns by clustering the streaklines. We then present a method to compare two videos by measuring the similarity between their motion patterns using a combination of shape theory and subspace analysis. Such an analysis allows us to represent, compare and recognize a wide range of activities. We perform experiments on state-of-the-art datasets and show that the proposed method is suitable for natural videos in the presence of noise, background clutter and high intra class variations. Our method has two significant advantages over recent related approaches - it provides a single framework that takes care of both low-level and high-level visual analysis tasks, and is computationally efficient.