Probabilistic visual learning for object representation

We present an unsupervised technique for visual learning, which is based on density estimation in high-dimensional spaces using an eigenspace decomposition. Two types of density estimates are derived for modeling the training data: a multivariate Gaussian (for unimodal distributions) and a mixture-of-Gaussians model (for multimodal distributions). Those probability densities are then used to formulate a maximum-likelihood estimation framework for visual search and target detection for automatic object recognition and coding. Our learning technique is applied to the probabilistic visual modeling, detection, recognition, and coding of human faces and nonrigid objects, such as hands     

Online multi-view subspace learning via group structure analysis for visual object tracking

In this paper, we focus on incrementally learning a robust multi-view subspace representation for visual object tracking. During the tracking process, due to the dynamic background variation and target appearance changing, it is challenging to learn an informative feature representation of tracking object, distinguished from the dynamic background. To this end, we propose a novel online multi-view subspace learning algorithm (OMEL) via group structure analysis, which consistently learns a low-dimensional representation shared across views with time changing. In particular, both group sparsity and group interval constraints are incorporated to preserve the group structure in the low-dimensional subspace, and our subspace learning model will be incrementally updated to prevent repetitive computation of previous data. We extensively evaluate our proposed OMEL on multiple benchmark video tracking sequences, by comparing with six related tracking algorithms. Experimental results show that OMEL is robust and effective to learn dynamic subspace representation for online object tracking problems. Moreover, several evaluation tests are additionally conducted to validate the efficacy of group structure assumption.     

Collaborative strategy for visual object tracking

Multimedia Tools and Applications - Adaptively learning the difference between object and background, discriminative trackers are able to overcome the complex background problem in visual object...     

Discriminative subspace learning with sparse representation view-based model for robust visual tracking

In this paper, we propose a robust tracking algorithm to handle drifting problem. This algorithm consists of two parts: the first part is the G&D part that combines Generative model and Discriminative model for tracking, and the second part is the View-Based model for target appearance that corrects the result of the G&D part if necessary. In G&D part, we use the Maximum Margin Projection (MMP) to construct a graph model to preserve both local geometrical and discriminant structures of the data manifold in low dimensions. Therefore, such discriminative subspace combined with traditional generative subspace can benefit from both models. In addition, we address the problem of learning maximum margin projection under the Spectral Regression (SR) which results in significant savings in computational time. To further solve the drift, an online learned sparsely represented view-based model of the target is complementary to the G&D part. When the result of G&D part is unreliable, the view-based model can rectify the result in order to avoid drifting. Experimental results on several challenging video sequences demonstrate the effectiveness and robustness of our approach.     

Adaptable deep learning structures for object labeling/tracking under dynamic visual environments

Multimedia Tools and Applications - In this paper, we propose a self-adaptive deep neural network architecture suitable for object tracking and labelling. In particular, an adaptation mechanism is...     

Learning structured visual dictionary for object tracking

In this paper, we propose a visual tracking algorithm by incorporating the appearance information gathered from two collaborative feature sets and exploiting its geometric structures. A structured visual dictionary (SVD) can be learned from both appearance and geometric structure, thereby enhancing its discriminative strength between the foreground object and the background. Experimental results show that the proposed tracking algorithm using SVD (SVDTrack) performs favorably against the state-of-the-art methods.     

Multi-task non-negative matrix factorization for visual object tracking

Pattern Analysis and Applications - This paper proposes an online object tracking algorithm in which the object tracking is achieved by using multi-task sparse learning and non-negative matrix...     

Illumination invariant object tracking with adaptive sparse representation

Since the introduction of the sparse representation-based tracking method named ?₁ tracker, there have been further studies into this tracking framework with promised results in challenging video sequences. However, in the situation of large illumination changes and shadow casting, the tracked object cannot be modeled efficiently by sparse representation templates. To overcome this problem, we propose a new illumination invariant tracker based on photometric normalization techniques and the sparse representation framework. With photometric normalization methods, we designed a new illumination invariant template presentation for tracking that eliminates the illumination influences, such as brightness variation and shadow casting. For a higher tracking accuracy, we introduced a strategy that adaptively selects the optimum template presentation at the update step of the tracking process. The experiments show that our approach outperforms the previous ?₁ and some state-of-the-art algorithms in tracking sequences with severe illumination effects.     

SiamMN: Siamese modulation network for visual object tracking

Multimedia Tools and Applications - Visual object tracking methods based on Siamese network are often difficult to distinguish objects with the same semantic or similar appearance as tracking...     

Graph-based transductive learning for robust visual tracking

In object tracking problem, most methods assume brightness constancy or subspace constancy, which are violated in practice. In this paper, the object tracking problem is considered as a transductive learning problem and a robust tracking method is proposed under intrinsic and extrinsic varieties. The object not only fits the object model, but also has the same cluster with the previous objects, which are the labeled data. By constraining the global and local information, the cost function is constructed firstly. The solution for minimizing the cost function can be solved by a simple linear algebra with graph Laplacian. Moreover, a novel graph is constructed over the positive samples and candidate patches, which can simultaneously learn the object's global appearance model and the local intrinsic geometric structure of all the patches. Furthermore, a heuristic positive samples selection scheme is adopted to make the method more effective. The proposed method is tested on different videos, which undergo large pose, expression, illumination and partial occlusion, and compared with state-of-the-art algorithms. Experimental results and comparative studies are provided to demonstrate the efficiency of the proposed method.     

Sparse Bayesian learning for efficient visual tracking

This paper extends the use of statistical learning algorithms for object localization. It has been shown that object recognizers using kernel-SVMs can be elegantly adapted to localization by means of spatial perturbation of the SVM. While this SVM applies to each frame of a video independently of other frames, the benefits of temporal fusion of data are well-known. This is addressed here by using a fully probabilistic relevance vector machine (RVM) to generate observations with Gaussian distributions that can be fused over time. Rather than adapting a recognizer, we build a displacement expert which directly estimates displacement from the target region. An object detector is used in tandem, for object verification, providing the capability for automatic initialization and recovery. This approach is demonstrated in real-time tracking systems where the sparsity of the RVM means that only a fraction of CPU time is required to track at frame rate. An experimental evaluation compares this approach to the state of the art showing it to be a viable method for long-term region tracking.     

Accurate object contour tracking based on boundary edge selection

In this paper, a novel method for accurate subject tracking, by selecting only tracked subject boundary edges in a video stream with a changing background and moving camera, is proposed. This boundary edge selection is achieved in two steps: (1) removing background edges using edge motion, and from the output of the previous step, (2) selecting boundary edges using a normal direction derivative of the tracked contour. Accurate tracking is based on reduction of the effects of irrelevant edges, by only selecting boundary edge pixels. In order to remove background edges using edge motion, the tracked subject motion is computed and edge motions and edges having different motion directions from the subjects are removed. In selecting boundary edges using the normal contour direction, the image gradient values on every edge pixel are computed, and edge pixels with large gradient values are selected. Multi-level Canny edge maps are used to obtain proper details of a scene. Multi-level edge maps allow tracking, even though the tracked object boundary has complex edges, since the detail level of an edge map for the scene can be adjusted. A process of final routing is deployed in order to obtain a detailed contour. The computed contour is improved by checking against a strong Canny edge map and hiring strong Canny edge pixels around the computed contour using Dijkstra's minimum cost routing. The experimental results demonstrate that the proposed tracking approach is robust enough to handle a complex-textured scene in a mobile camera environment.     

Robust object tracking with RGBD-based sparse learning

Robust object tracking has been an important and challenging research area in the field of computer vision for decades. With the increasing popularity of affordable depth sensors, range data is widely used in visual tracking for its ability to provide robustness to varying illumination and occlusions. In this paper, a novel RGBD and sparse learning based tracker is proposed. The range data is integrated into the sparse learning framework in three respects. First, an extra depth view is added to the color image based visual features as an independent view for robust appearance modeling. Then, a special occlusion template set is designed to replenish the existing dictionary for handling various occlusion conditions. Finally, a depth-based occlusion detection method is proposed to efficiently determine an accurate time for the template update. Extensive experiments on both KITTI and Princeton data sets demonstrate that the proposed tracker outperforms the state-of-the-art tracking algorithms, including both sparse learning and RGBD based methods.     

Accurate keyframe selection and keypoint tracking for robust visual odometry

This paper presents a novel stereo visual odometry (VO) framework based on structure from motion, where a robust keypoint tracking and matching is combined with an effective keyframe selection strategy. In order to track and find correct feature correspondences a robust loop chain matching scheme on two consecutive stereo pairs is introduced. Keyframe selection is based on the proportion of features with high temporal disparity. This criterion relies on the observation that the error in the pose estimation propagates from the uncertainty of 3D points—higher for distant points, that have low 2D motion. Comparative results based on three VO datasets show that the proposed solution is remarkably effective and robust even for very long path lengths.     

Spatial and semantic convolutional features for robust visual object tracking

Multimedia Tools and Applications - Robust and accurate visual tracking is a challenging problem in computer vision. In this paper, we exploit spatial and semantic convolutional features extracted...     

Exploiting weak mask representation with convolutional neural networks for accurate object tracking

Multimedia Tools and Applications - Recent years have witnessed the popularity of Convolutional Neural Networks (CNN) in a variety of computer vision tasks, including video object tracking....     

M-PFGMNet: multi-pose feature generation mapping network for visual object tracking

Multimedia Tools and Applications - The occlusion and low-resolution environments lead to insufficient feature data and redundant calculations, which affects the accuracy and timeliness of the...     

Convolutional restricted Boltzmann machines learning for robust visual tracking

It is a critical step to choose visual features in object tracking. Most existing tracking approaches adopt handcrafted features, which greatly depend on people’s prior knowledge and easily become invalid in other conditions where the scene structures are different. On the contrary, we learn informative and discriminative features from image data of tracking scenes itself. Local receptive filters and weight sharing make the convolutional restricted Boltzmann machines (CRBM) suit for natural images. The CRBM is applied to model the distribution of image patches sampled from the first frame which shares same properties with other frames. Each hidden variable corresponding to one local filter can be viewed as a feature detector. Local connections to hidden variables and max-pooling strategy make the extracted features invariant to shifts and distortions. A simple naive Bayes classifier is used to separate object from background in feature space. We demonstrate the effectiveness and robustness of our tracking method in several challenging video sequences. Experimental results show that features automatically learned by CRBM are effective for object tracking.     

Semisupervised online learning of hierarchical structures for visual object classification

Multimedia Tools and Applications - One of the main challenges in hierarchical object classification is the derivation of the correct hierarchical structure. The classic way around the problem is...