Estimating Dynamic Flow Features in Groups of Tracked Objects

• URL: http://arxiv.org/abs/2408.16190v1
• Date: Thu, 29 Aug 2024 01:06:51 GMT
• Title: Estimating Dynamic Flow Features in Groups of Tracked Objects
• Authors: Tanner D. Harms, Steven L. Brunton, Beverley J. McKeon,
• Abstract summary: This work aims to extend gradient-based dynamical systems analyses to real-world applications characterized by complex, feature-rich image sequences with imperfect tracers. The proposed approach is affordably implemented and enables advanced studies including the motion analysis of two distinct object classes in a single image sequence.
• Score: 2.4344640336100936
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Interpreting motion captured in image sequences is crucial for a wide range of computer vision applications. Typical estimation approaches include optical flow (OF), which approximates the apparent motion instantaneously in a scene, and multiple object tracking (MOT), which tracks the motion of subjects over time. Often, the motion of objects in a scene is governed by some underlying dynamical system which could be inferred by analyzing the motion of groups of objects. Standard motion analyses, however, are not designed to intuit flow dynamics from trajectory data, making such measurements difficult in practice. The goal of this work is to extend gradient-based dynamical systems analyses to real-world applications characterized by complex, feature-rich image sequences with imperfect tracers. The tracer trajectories are tracked using deep vision networks and gradients are approximated using Lagrangian gradient regression (LGR), a tool designed to estimate spatial gradients from sparse data. From gradients, dynamical features such as regions of coherent rotation and transport barriers are identified. The proposed approach is affordably implemented and enables advanced studies including the motion analysis of two distinct object classes in a single image sequence. Two examples of the method are presented on data sets for which standard gradient-based analyses do not apply.

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