A survey of datasets for visual tracking <s> The Oron dataset <s> Recently sparse representation has been applied to visual tracker by modeling the target appearance using a sparse approximation over a template set, which leads to the so-called L1 trackers as it needs to solve an l 1 norm related minimization problem for many times. While these L1 trackers showed impressive tracking accuracies, they are very computationally demanding and the speed bottleneck is the solver to l 1 norm minimizations. This paper aims at developing an L1 tracker that not only runs in real time but also enjoys better robustness than other L1 trackers. In our proposed L1 tracker, a new l 1 norm related minimization model is proposed to improve the tracking accuracy by adding an l 1 norm regularization on the coefficients associated with the trivial templates. Moreover, based on the accelerated proximal gradient approach, a very fast numerical solver is developed to solve the resulting l 1 norm related minimization problem with guaranteed quadratic convergence. The great running time efficiency and tracking accuracy of the proposed tracker is validated with a comprehensive evaluation involving eight challenging sequences and five alternative state-of-the-art trackers. <s> BIB001 </s> A survey of datasets for visual tracking <s> The Oron dataset <s> We propose a novel tracking algorithm based on the Wang-Landau Monte Carlo (WLMC) sampling method for dealing with abrupt motions efficiently. Abrupt motions cause conventional tracking methods to fail because they violate the motion smoothness constraint. To address this problem, we introduce the Wang-Landau sampling method and integrate it into a Markov Chain Monte Carlo (MCMC)-based tracking framework. By employing the novel density-of-states term estimated by the Wang-Landau sampling method into the acceptance ratio of MCMC, our WLMC-based tracking method alleviates the motion smoothness constraint and robustly tracks the abrupt motions. Meanwhile, the marginal likelihood term of the acceptance ratio preserves the accuracy in tracking smooth motions. The method is then extended to obtain good performance in terms of scalability, even on a high-dimensional state space. Hence, it covers drastic changes in not only position but also scale of a target. To achieve this, we modify our method by combining it with the N-fold way algorithm and present the N-Fold Wang-Landau (NFWL)-based tracking method. The N-fold way algorithm helps estimate the density-of-states with a smaller number of samples. Experimental results demonstrate that our approach efficiently samples the states of the target, even in a whole state space, without loss of time, and tracks the target accurately and robustly when position and scale are changing severely. <s> BIB002 </s> A survey of datasets for visual tracking <s> The Oron dataset <s> Recently, sparse representation in the task of visual tracking has been obtained increasing attention and many algorithms are proposed based on it. In these algorithms for visual tracking, each candidate target is sparsely represented by a set of target templates. However, these algorithms fail to consider the structural information of the space of the target templates, i.e., target template set. In this paper, we propose an algorithm named non-local self-similarity (NLSS) based sparse coding algorithm (NLSSC) to learn the sparse representations, which considers the geometrical structure of the set of target candidates. By using non-local self-similarity (NLSS) as a smooth operator, the proposed method can turn the tracking into sparse representations problems, in which the information of the set of target candidates is exploited. Extensive experimental results on visual tracking have demonstrated the effectiveness of the proposed algorithm. <s> BIB003 </s> A survey of datasets for visual tracking <s> The Oron dataset <s> Multi-target visual tracking is a challenge because of data association and mutual occlusion in the interacting targets. This paper presents a Gaussian mixture probability hypothesis density based multi-target visual tracking system with game-theoretical occlusion handling. Firstly, the spatial constraint based appearance model with other interacting targets' interferences is modeled. Then, a two-step occlusion reasoning algorithm is proposed. Finally, an n-person, non-zero-sum, non-cooperative game is constructed to handle the mutual occlusion problem. The individual targets within the occlusion region are regarded as the players in the constructed game to compete for the maximum utilities by using the certain strategies. A Nash Equilibrium of the game is the optimal estimation of the locations of the players within the occlusion region. Experiments on video sequences demonstrate the good performance of the proposed occlusion handling algorithm. <s> BIB004 </s> A survey of datasets for visual tracking <s> The Oron dataset <s> Locally Orderless Tracking (LOT) is a visual tracking algorithm that automatically estimates the amount of local (dis)order in the target. This lets the tracker specialize in both rigid and deformable objects on-line and with no prior assumptions. We provide a probabilistic model of the target variations over time. We then rigorously show that this model is a special case of the Earth Mover's Distance optimization problem where the ground distance is governed by some underlying noise model. This noise model has several parameters that control the cost of moving pixels and changing their color. We develop two such noise models and demonstrate how their parameters can be estimated on-line during tracking to account for the amount of local (dis)order in the target. We also discuss the significance of this on-line parameter update and demonstrate its contribution to the performance. Finally we show LOT's tracking capabilities on challenging video sequences, both commonly used and new, displaying performance comparable to state-of-the-art methods. <s> BIB005
The them in Fig. 16 ) with deforming objects whose appearances drastically change over time (see in particular the Shirt sequence). All sequences are provided with a ground truth corresponding to the center and size of the object's bounding box in each frame. This dataset was used by different works BIB005  BIB002 .  The Litiv dataset [31] . Four sequences with strong occlusions, similar appearances and cluttered backgrounds objectives were to track and count people in crowds to estimate their density, and to detect events by crowd analysis. In 2006 [97] a left luggage detection in public area competition was also proposed. For these two competitions, two benchmark data were proposed, corresponding to multi sensor sequences containing different activities, and different scenarios. They were filmed by several synchronized cameras. Some frames of the sequences proposed in 2006 and 2013 are given in Fig. 17 . Calibration data are also given. Note that, due to the competition's purpose, almost no ground truth is available. However, for some of the sequences, people have provided some, see for instance [36, 98] . PETS datasets are very popular among the computer vision community. Some proposed scenarios address hard challenges for the visual tracking community, such as similarity of appearance of the tracked objects, changes in illumination (for the outdoor sequences) or occlusions. Some recent algorithms were tested on some sequences of these datasets BIB005 BIB001 BIB003 BIB004  .