Pose-Configurable Generic Tracking of Elongated Objects

Abstract

Elongated objects have various shapes and can shift, rotate, change scale, and be rigid or deform by flexing, articulating, and vibrating, with examples as varied as a glass bottle, a robotic arm, a surgical suture, a finger pair, a tram, and a guitar string. This generally makes tracking of poses of elongated objects very challenging.We describe a unified, configurable framework for tracking the pose of elongated objects, which move in the image plane and extend over the image region. Our method strives for simplicity, versatility, and efficiency. The object is decomposed into a chained assembly of segments of multiple parts that are arranged under a hierarchy of tailored spatio-temporal constraints. In this hierarchy, segments can rescale independently while their elasticity is controlled with global orientations and local distances.While the trend in tracking is to design complex, structure-free algorithms that update object appearance online, we show that our tracker, with the novel but remarkably simple, structured organization of parts with constant appearance, reaches or improves state-of-the-art performance. Most importantly, our model can be easily configured to track exact pose of arbitrary, elongated objects in the image plane. The tracker can run up to 100 fps on a desktop PC, yet the computation time scales linearly with the number of object parts. To our knowledge, this is the first approach to generic tracking of elongated objects.

Surgical tool tracking by on-line selection of structural correlation filters

“…Our visual tracking problem is formulated within the Bayesian inference framework [7], [20] with spatio-temporal constraints [18], [19]. Similar to [23], we use the affine motion model with six parameters to describe the object's state u t = [p, r 1 , a 1 , r 2 , r 3 , a 2 ].…”

“…We propose a unified objective function to integrate these two sparse representation problems together. The function combines spatio-temporal constraints related to shaft and end-effector [18], [19], [20] with confidence maps of co-appearance of different part together. The resulting optimization problem can be well solved by efficient dynamic programming.…”

Local pose estimation of feature points for object based augmented reality.

“…Other type of constraint, that several works assume for deformable object pose estimation, are that object are piece-wise rigid or articulated (CHANG;ZWICKER, 2011;WESIERSKI;HORAIN, 2013;MICHEL et al, 2015;PAUWELS;RUBIO;ROS, 2014;PEKELNY;GOTSMAN, 2008). In this constraint, the object is assumed to have rigid parts, that can be matched across different views.…”