text: Abstract In this paper, we propose a novel approach for exploiting structural relations to track multiple objects that may undergo long-term occlusion and abrupt motion. We use a model-free approach that relies only on annotations given in the first frame of the video to track all the objects online, i.e. without knowledge from future frames. We initialize a probabilistic Attributed Relational Graph (ARG) from the first frame, which is incrementally updated along the video. Instead of using the structural information only to evaluate the scene, the proposed approach considers it to generate new tracking hypotheses. In this way, our method is capable of generating relevant object candidates that are used to improve or recover the track of lost objects. The proposed method is evaluated on several videos of table tennis, volleyball, and on the ACASVA dataset. The results show that our approach is very robust, flexible and able to outperform other state-of-the-art methods in sports videos that present structural patterns.
  @cite_15: Objects are usually embedded into context. Visual context has been successfully used in object detection tasks, however, it is often ignored in object tracking. We propose a method to learn supporters which are, be it only temporally, useful for determining the position of the object of interest. Our approach exploits the General Hough Transform strategy. It couples the supporters with the target and naturally distinguishes between strongly and weakly coupled motions. By this, the position of an object can be estimated even when it is not seen directly (e.g., fully occluded or outside of the image region) or when it changes its appearance quickly and significantly. Experiments show substantial improvements in model-free tracking as well as in the tracking of “virtual” points, e.g., in medical applications.
summary: Although similar, their proposal differs from the one presented in this paper in the following aspects: (1) their structural model only computes the difference between the observed distance and an ideal value that comes from the online training. Our model considers both distance and angle information obtained from a more general probability density function. (2) Although they use the structure to improve tracking and to deal with occlusion, it is not used to guide the detection process, which could lead to improved performance by restricting the search space. Our approach, inspired by @cite_15 , uses some objects as supports for the structural model to generate candidates of where the target is likely to be found after tracking loss. (3) Their method of tracking by detection does not consider motion models, while we rely on particle filters.