Inverse Sparse Representation Research Articles

Robust Object Tracking via Reverse Low-Rank Sparse Learning and Fractional-Order Variation Regularization

Object tracking based on low-rank sparse learning usually makes the drift phenomenon occur when the target faces severe occlusion and fast motion. In this paper, we propose a novel tracking algorithm via reverse low-rank sparse learning and fractional-order variation regularization. Firstly, we utilize convex low-rank constraint to force the appearance similarity of the candidate particles, so as to prune the irrelevant particles. Secondly, fractional-order variation is introduced to constrain the sparse coefficient difference in the bounded variation space, which allows the difference between consecutive frames to exist, so as to adapt object fast motion. Meanwhile, fractional-order regularization can restrain severe occlusion by considering more adjacent frames information. Thirdly, we employ an inverse sparse representation method to model the relationship between target candidates and target template, which can reduce the computation complexity for online tracking. Finally, an online updating scheme based on alternating iteration is proposed for tracking computation. Experiments on benchmark sequences show that our algorithm outperforms several state-of-the-art methods, especially exhibiting better adaptability for fast motion and severe occlusion.

Robust Visual Object Tracking With Multiple Features and Reliable Re-Detection Scheme

In recent years, correlation filter based trackers have seen widespread success because of their high efficiency and robustness. However, a single feature based tracker cannot deal with complex scenes such as serious occlusion, motion blur and illumination variation. In this paper, we develop a novel tracking method combining color feature, Hog feature and motion feature. The motion feature is estimated between adjacent frames by large displacement optical flow. Besides, in order to cope with boundary effect existing in traditional correlation filter based trackers, an adaptive cosine window is introduced in our method, which can highlight the target region, suppress the background region and enlarge search region. Meanwhile, a novel judge scheme combining Hog correlation response and color response is adopted to evaluate the reliability of tracking result. Finally, inverse sparse representation is presented to locate coarse positions of target in case of tracking failures. Extensive experiments on five famous tracking benchmarks including OTB100, TColor-128, UAVDT, UAV123 and VOT2016 demonstrate our proposed method outperform other sate-of-the-art methods in terms of robustness and accuracy.

Structural Regression Model Based Inverse Sparse Representation for Tracking Objects

In order to reduce the calculation cost and improve the robustness of appearance model, this paper presents an optimal object tracking method that consists of improved inverse sparse representation and global spatial envelope. First, partial least squares regression-based structural model is adopted, which easily facilitates target template sparsely represented by candidate dictionary. Furthermore, candidates with nonzero coefficients are easily selected as possible tracking results. Meanwhile, partial occlusion and slight appearance changes are effectively alleviated during the tracking process. Second, spatial envelope in the frequency domain is utilized to select the best candidate from the inverse sparse representation process. Multiple scales and orientations-based Gabor filters are established to obtain the Gist information, which keeps the potential structural attributes of local appearance models to tolerate appearance variation. In addition, the Bayesian inference framework is used to exploit candidate samples, and a simple model update scheme is employed to alleviate drifting caused by temporal varying multi-factors. The qualitative experimental results show that the proposed tracking algorithm provides a better performance in some dynamic scenes.

Object tracking via inverse sparse representation and convolutional networks

In this paper, we present a novel object tracking method based on inverse sparse representation and convolutional networks. First, in contrast to existing trackers based on conventional sparse representation, the target template can be sparsely represented by candidate dictionary in our method and the candidates corresponding to nonzero coefficients are selected as the some optimal candidates of tracking results. At the same time, locally normalized features are adopted to obtain the representation of target template and candidate dictionary, which can deal with partial occlusion and slight object appearance change. Second, a convolutional network is proposed to select the best candidate from the candidate set got by inverse sparse representation. Numerous bank filters are introduced to preserve local structure of the target and background samples and the feature maps are extracted to form the simple layers and complex layers. Finally, a simple local model update scheme is employed to accommodate occlusion and target appearance change. Both qualitative and quantitative evaluations on several challenging video sequences demonstrate that the proposed method can achieve favorable and stable results compared to the state-of-the-art trackers.

Object Tracking based on Relaxed Inverse Sparse Representation

In this paper, we develop a novel object tracking method based on sparse representation. First, we propose a relaxed sparse representation model, based on which the tracking problem is casted as an inverse sparse representation process. In this process, the target template is able to be sparsely approximated by all candidate samples. Second, we present an objective function that combines the sparse representation process of different fragments, the relaxed representation scheme and a weight reference prior. Based on some propositions, the proposed objective function can be solved by using an iteration algorithm. In addition, we design a tracking framework based on the proposed representation model and a simple online update manner. Finally, numerous experiments are conducted on some challenging sequences to compare our tracking method with some state-of-the-art ones. Both qualitative and quantitative results demonstrate that the proposed tracking method performs better than other competing algorithms.

Inverse sparse tracker with a locally weighted distance metric.

Sparse representation has been recently extensively studied for visual tracking and generally facilitates more accurate tracking results than classic methods. In this paper, we propose a sparsity-based tracking algorithm that is featured with two components: 1) an inverse sparse representation formulation and 2) a locally weighted distance metric. In the inverse sparse representation formulation, the target template is reconstructed with particles, which enables the tracker to compute the weights of all particles by solving only one l1 optimization problem and thereby provides a quite efficient model. This is in direct contrast to most previous sparse trackers that entail solving one optimization problem for each particle. However, we notice that this formulation with normal Euclidean distance metric is sensitive to partial noise like occlusion and illumination changes. To this end, we design a locally weighted distance metric to replace the Euclidean one. Similar ideas of using local features appear in other works, but only being supported by popular assumptions like local models could handle partial noise better than holistic models, without any solid theoretical analysis. In this paper, we attempt to explicitly explain it from a mathematical view. On that basis, we further propose a method to assign local weights by exploiting the temporal and spatial continuity. In the proposed method, appearance changes caused by partial occlusion and shape deformation are carefully considered, thereby facilitating accurate similarity measurement and model update. The experimental validation is conducted from two aspects: 1) self validation on key components and 2) comparison with other state-of-the-art algorithms. Results over 15 challenging sequences show that the proposed tracking algorithm performs favorably against the existing sparsity-based trackers and the other state-of-the-art methods.