Real-Time Salient Closed Boundary Tracking via Line Segments Perceptual Grouping
This addresses the problem of real-time boundary tracking for applications like robotics, though it appears incremental as it builds on existing perceptual grouping methods.
The paper tackles real-time tracking of salient closed boundaries in video by integrating tracking into a perceptual grouping framework, achieving robust performance with a new criterion and a graph-based algorithm. It demonstrates efficiency and robustness on real video sequences and a robot arm pouring experiment.
This paper presents a novel real-time method for tracking salient closed boundaries from video image sequences. This method operates on a set of straight line segments that are produced by line detection. The tracking scheme is coherently integrated into a perceptual grouping framework in which the visual tracking problem is tackled by identifying a subset of these line segments and connecting them sequentially to form a closed boundary with the largest saliency and a certain similarity to the previous one. Specifically, we define a new tracking criterion which combines a grouping cost and an area similarity constraint. The proposed criterion makes the resulting boundary tracking more robust to local minima. To achieve real-time tracking performance, we use Delaunay Triangulation to build a graph model with the detected line segments and then reduce the tracking problem to finding the optimal cycle in this graph. This is solved by our newly proposed closed boundary candidates searching algorithm called "Bidirectional Shortest Path (BDSP)". The efficiency and robustness of the proposed method are tested on real video sequences as well as during a robot arm pouring experiment.