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_31: 
  @cite_21: Robust tracking of abrupt motion is a challenging task in computer vision due to the large motion uncertainty. In this paper, we propose a stochastic approximation Monte Carlo (SAMC) based tracking scheme for abrupt motion problem in Bayesian filtering framework. In our tracking scheme, the particle weight is dynamically estimated by learning the density of states in simulations, and thus the local-trap problem suffered by the conventional MCMC sampling-based methods could be essentially avoided. In addition, we design an adaptive SAMC sampling method to further speed up the sampling process for tracking of abrupt motion. It combines the SAMC sampling and a density grid based statistical predictive model, to give a data-mining mode embedded global sampling scheme. It is computationally efficient and effective in dealing with abrupt motion difficulties. We compare it with alternative tracking methods. Extensive experimental results showed the effectiveness and efficiency of the proposed algorithm in dealing with various types of abrupt motions.
  @cite_6: Abrupt motion is a significant challenge that commonly causes traditional tracking methods to fail. This paper presents an improved visual saliency model and integrates it to a particle filter tracker to solve this problem. Once the target is lost, our algorithm recovers tracking by detecting the target region from salient regions, which are obtained in the saliency map of current frame. In addition, to strengthen the saliency of target region, the target model is used as a prior knowledge to calculate a weight set which is utilized to construct our improved saliency map adaptively. Furthermore, we adopt the covariance descriptor as the appearance model to describe the object more accurately. Compared with several other tracking algorithms, the experimental results demonstrate that our method is more robust in dealing with various types of abrupt motion scenarios.
  @cite_3: We propose a novel tracking algorithm based on the Wang-Landau Monte Carlo sampling method which efficiently deals with the abrupt motions. Abrupt motions could cause conventional tracking methods to fail since they violate the motion smoothness constraint. To address this problem, we introduce the Wang-Landau algorithm that has been recently proposed in statistical physics, and integrate this algorithm into the Markov Chain Monte Carlo based tracking method. Our tracking method alleviates the motion smoothness constraint utilizing both the likelihood term and the density of states term, which is estimated by the Wang-Landau algorithm. The likelihood term helps to improve the accuracy in tracking smooth motions, while the density of states term captures abrupt motions robustly. Experimental results reveal that our approach efficiently samples the object's states even in a whole state space without loss of time. Therefore, it tracks the object of which motion is drastically changing, accurately and robustly.
summary: Another important issue that must be dealt with during tracking is abrupt motion. Perhaps, the simplest way to deal with it is by generating some additional target location hypotheses around the previous location to try to cover a broader region, as explored in @cite_31 . Another proposal is to solve the same problem using spatial position information for finding better candidates @cite_3 @cite_21 . This is done by dividing the image into several smaller regions and using the information obtained from the density of states of each one to find new locations. More recently, Su al @cite_6 propose relying on visual saliency information to guide the tracking and restore the target. It is important to note that, although tracking-by-detection methods should be able to deal with abrupt motion, most of them do not behave well in this situation because their association function usually enforces the temporal stability constraint. As previously mentioned, we use a different approach that relies on the structural relations between the objects to find the most likely new locations of a lost target.