Tracking of Moving Objects in Video Through Invariant Features in Their Graph Representation

Abstract

The paper suggests a contour-based algorithm for tracking moving objects in video. The inputs are segmented moving objects. Each segmented frame is transformed into region adjacency graphs (RAGs). The object's contour is divided into subcurves. Contour's junctions are derived. These junctions are the unique “signature” of the tracked object. Junctions from two consecutive frames are matched. The junctions' motion is estimated using RAG edges in consecutive frames. Each pair of matched junctions may be connected by several paths (edges) that become candidates that represent a tracked contour. These paths are obtained by the k-shortest paths algorithm between two nodes. The RAG is transformed into a weighted directed graph. The final tracked contour construction is derived by a match between edges (subcurves) and candidate paths sets. The RAG constructs the tracked contour that enables an accurate and unique moving object representation. The algorithm tracks multiple objects, partially covered (occluded) objects, compounded object of merge/split such as players in a soccer game and tracking in a crowded area for surveillance applications. We assume that features of topologic signature of the tracked object stay invariant in two consecutive frames. The algorithm's complexity depends on RAG's edges and not on the image's size.