Object Tracking based on Features and Structures

Nicole M. Artner and Walter G. Kropatsch Vienna University of Technology Pattern Recognition and Image Processing Group {artner,krw}@prip.tuwien.ac.at I.

I NTRODUCTION

In recent years, we have been studying the potential of graph-based methods and representations in the field of video object tracking [1], [2]. Our aim is to track rigid (e.g. manmade objects) and articulated objects (e.g. humans) through challenging situations like distractors and occlusions. Furthermore, the output should go beyond a single trajectory of the center of mass of the object. Especially for articulated objects, we are interested in the local motion of the parts of the object (e.g. limbs of a human body). To achieve this aim, we propose to represent and to track target objects by a combination of appearance and structure. II.

G RAPH MODEL

Graph models offer high representational power and are an elegant way to represent various kinds of information. We use attributed graphs to describe the target objects’ appearance and structure. An attributed graph consists of a set of vertices connected via a set of edges.In our graph model, the vertices represent and store discriminative features extracted from a region of interest covering the target object. The edges encode the structure of the target object, which describes the spatial relationships and dependencies of the vertices. There are different possibilities to insert the edges into the graph like the Delaunay triangulation. A rigid target object can be represented by a single attributed graph [2]. For an articulated object, we use one attributed graph for each rigid part (e.g. for each body part of a human). These attributed graphs are connected via points of articulation, which are used to transfer information between adjacent parts. For efficiency purposes, we also experimented with a hierarchical graph model (attributed graph pyramids) [1]. III.

We did experimental evaluations on two kinds of structural cue [2]. The first one is based on graph relaxation giving the edges a “spring-like” behavior. Hence, a deviation in the edge lengths stored in the model can be used to calculate a structural cue from the energies in the “springs” (edges). The second one is based on barycentric coordinates. When the graph model is a triangulated graph, it is possible to calculate a structural cue for each vertex with the help of the barycentric coordinates of any triangle in the graph. IV.

Our research was inspired by the work of Fischler et al. on pictorial structures in 1973. They represent an object by a set of parts in a deformable configuration. Felzenszwalb et al. [4] continued and improved the ideas of Fischler et al. to do part-based object recognition. In comparison to these works, our approach does not depend on training, is not limited to acyclic graphs, differently interprets the cost function for the optimization, and is applied in tracking. The proposed tracking approach is based on the Mean Shift algorithm [3], but it offers substantial novelty. The iterative mode seeking processes of the Mean Shift trackers are linked together and influence each other. Furthermore, the proposed tracking integrates structure and it dynamically determines the combination of both cues in each vertex. V.

C ONCLUSIONS AND OPEN ISSUES

The presented approach is able to track rigid and articulated objects through 2D translation, rotation and scaling. Due to the combination of appearance and structure, it is possible for a modified Mean Shift tracker to overcome challenging situations (e.g. occlusions, distractors, motion blur, etc.). The output of the approach is composed of several trajectories for each rigid object/part. Our approach is flexible with regard to the target object and the employed features. Currently, the proposed approach is not able to deal with motion in 3D and non-rigid deformations (e.g. flag waving in the wind). Hence, open issues are additional structural cues and elaborate update mechanisms for the graph model.

C OMBINED ITERATIVE TRACKING

The Mean Shift algorithm is a well-known and thoroughly researched approach for tracking [3]. By applying a Mean Shift tracker on each vertex in a graph model, it is possible to track them based on their appearance information. The iterative mode seeking process of Mean Shift finds the position with the highest similarity in appearance to the model. It provides an appearance cue in the form of an offset vector pointing towards a certain position. In addition to the appearance cue, we also want to consider a structural cue, which is deduced from the graph models’ edges. We propose a novel combined iterative tracking approach. This approach locally maximizes the similarity in appearance and additionally minimizes the deviation from the structure in the graph model.

R ELATED WORK

R EFERENCES [1]

[2]

[3]

[4]

N. M. Artner, A. Ion, and W. G. Kropatsch, “Multi-scale 2d tracking of articulated objects using hierarchical spring systems,” Pattern Recognition, vol. 44, no. 4, pp. 800–810, April 2011. N. M. Artner and W. G. Kropatsch, “Structural cues in 2d tracking: Edge lengths vs. barycentric coordinates,” in 18th Iberoamerican Congress on Pattern Recognition, ser. Lecture Notes in Computer Science. Springer, November 2013, pp. 503–511. D. Comaniciu, V. Ramesh, and P. Meer, “Kernel-based object tracking,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 5, pp. 564–575, 2003. P. Felzenszwalb, R. Girshick, D. McAllester, and D. Ramanan, “Object detection with discriminatively trained part-based models,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 9, pp. 1627–1645, 2010.

Object Tracking based on Features and Structures

appearance and structure. II. GRAPH MODEL. Graph models offer high representational power and are an elegant way to represent various kinds of information.

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