Robust Visual Tracking via Exclusive Context Modeling.

In this paper, we formulate particle filter based tracking as a multi-task sparse learning problem that exploits context information. The target and context information which modeled as linear combinations of principal component analysis (PCA) basis is formed as dictionary templates. We treat the dictionary templates as the guidance and the incoming candidates are filtered depending on the similarity between the guidance image and each input. The guided filter can help to distinguish the target from numerous candidates via context information. Then multi-task sparse learning is employed to learn the target and context information. The proposed learning problem is efficiently solved using an alternating direction method of multipliers (ADMM) method that yield a sequence of closed form updates. We test our tracker on challenging benchmark sequences that involve drastic illumination changes, large pose variations, and heavy occlusion. Experimental results show that our tracker consistently outperforms state-of-the-art trackers.

This paper describes an approach for lip tracking using dynamic deformable templates. The objective is to track lip parameters from an audio-visual corpus recording a voice talent who is reading text prompts in a natural and expressive way. The corpus presents challenges to the conventional method of lip tracking with deformable templates. This is because natural and expressive speech includes relatively large motions of the head and the lips. The head motions lead to changes in the illumination of the face region and changes in the observed lip shape. In addition, emphatic pronunciations lead to large changes in the lip shape. Video frames that are affected by face illumination changes present additional difficulty in locating the mouth region (i.e. region of interest, ROI). Video frames that are affected by changes in lip shapes present additional deviations from the lip templates and hence lower tracking accuracies. Our proposed method incorporates dynamicity in the deformable templates to render them adaptive to changes in head pose, face illumination and lip shapes. Experiments show that dynamic deformable templates consistently outperform the conventional deformable templates in lip tracking.

Sparse representation based methods have recently drawn much attention in visual tracking due to good performance against illumination variation and occlusion. They assume the errors caused by image variations can be modeled as pixel-wise sparse. However, in many practical scenarios these errors are not truly pixel-wise sparse but rather sparsely distributed in a structured way. In fact, pixels in error constitute contiguous regions within the object's track. This is the case when significant occlusion occurs. To accommodate for non-sparse occlusion in a given frame, we assume that occlusion detected in previous frames can be propagated to the current one. This propagated information determines which pixels will contribute to the sparse representation of the current track. In other words, pixels that were detected as part of an occlusion in the previous frame will be removed from the target representation process. As such, this paper proposes a novel tracking algorithm that models and detects occlusion through structured sparse learning. We test our tracker on challenging benchmark sequences, such as sports videos, which involve heavy occlusion, drastic illumination changes, and large pose variations. Experimental results show that our tracker consistently outperforms the state-of-the-art. © 2013 IEEE.

This paper describes an approach for lip tracking using dynamic deformable templates. The objective is to track lip parameters from an audio-visual corpus recording a voice talent who is reading text prompts in a natural and expressive way. The corpus presents challenges to the conventional method of lip tracking with deformable templates. This is because natural and expressive speech includes relatively large motions of the head and the lips. The head motions lead to changes in the illumination of the face region and changes in the observed lip shape. In addition, emphatic pronunciations lead to large changes in the lip shape. Video frames that are affected by face illumination changes present additional difficulty in locating the mouth region (i.e. region of interest, ROI). Video frames that are affected by changes in lip shapes present additional deviations from the lip templates and hence lower tracking accuracies. Our proposed method incorporates "dynamicity" in the deformable templates to render them adaptive to changes in head pose, face illumination and lip shapes. Experiments show that dynamic deformable templates consistently outperform the conventional deformable templates in lip tracking.

Recently a category of tracking methods based on "tracking-by-detection" is widely used in visual tracking problem. Most of these methods update the classifier online using the samples generated by the tracker to handle the appearance changes. However, the self-updating scheme makes these methods suffer from drifting problem because of the incorrect labels of weak classifiers in training samples. In this paper, we split the class labels into true labels and noise labels and model them by sparse representation. A novel dynamic classifier selection method, robust to noisy training data, is proposed. Moreover, we apply the proposed classifier selection algorithm to visual tracking by integrating a part based online boosting framework. We have evaluated our proposed method on 12 challenging sequences involving severe occlusions, significant illumination changes and large pose variations. Both the qualitative and quantitative evaluations demonstrate that our approach tracks objects accurately and robustly and outperforms state-of-the-art trackers.

We propose a novel multiple object tracking algorithm in a particle filter framework, where the input is a set of candidate regions obtained from Robust Principle Component Analysis (RPCA) in each frame, and the goals is to recover trajectories of objects over time. Our method adapts to the changing appearance of objects, due to occlusion, illumination changes and large pose variations, by incorporating a <i>l</i>1 minimization-based appearance model into the Maximize A Posterior (MAP) inference. Though L1 trackers have showed impressive tracking accuracy, they are computationally demanding for multiple object tracking. Conventional data association methods using simple nonparametric appearance model, such as histogram-based descriptor, may suffer from drastic changing object appearance. The robust tracking performance of our approach has been validated with a comprehensive evaluation involving several challenging sequences and state-of-the-art multiple object trackers.

In order to realize robust visual tracking in natural environments, a novel algorithm based on adaptive appearance model is proposed. The model can adapt to changes in object appearance over time. A mixture of three Gaussian distributions models the value of each pixel. An online Expectation Maximization (EM) algorithm is developed to update the parameters of the Gaussians. The observation model in the particle filter is designed based on the adaptive appearance model. Numerous experimental results demonstrate that our proposed algorithm can track objects well under illumination change, large pose variation, and partial or full occlusion.Keywordsvisual trackingadaptive appearance modelExpectation Maximization algorithmparticle filter

We propose a model for extracting facial features robustly for face recognition under large pose variations in videos. The facial features are retrieved via Gabor Wavelet Transform with an embedded Hidden Markov Model (HMM), which decodes each observed face image into a state sequence. While an HMM can segment images into features at a fixed pose, multiple HMMs are trained for each individual to extract features robustly under large pose variation. The effectiveness of the proposed approach is validated through using the Sheffield Face Database. Our experiment shows better result than several other methods such as DCT+HMM,DWT+HMM, etc.

A Cluster-Based Competitive Particle Swarm Optimizer with a Sparse Truncation Operator for Multi-Objective Optimization

Ship detection using synthetic aperture radar (SAR) has been extensively utilized in both the military and civilian fields. On account of complex backgrounds, large scale variations, small-scale targets, and other challenges, it is difficult for current SAR ship detection methods to strike a balance between detection accuracy and computation efficiency. To overcome those challenges, ESarDet, an efficient SAR ship detection method based on contextual information and a large effective receptive field (ERF), is proposed. We introduce the anchor-free object detection method YOLOX-tiny as a baseline model and make several improvements to it. First, CAA-Net, which has a large ERF, is proposed to better merge the contextual and semantic information of ships in SAR images to improve ship detection, particularly for small-scale ships with complex backgrounds. Further, to prevent the loss of semantic information regarding ship targets in SAR images, we redesign a new spatial pyramid pooling network, namely A2SPPF. Finally, in consideration of the challenge posed by the large variation in ship scale in SAR images, we design a novel convolution block, called A2CSPlayer, to enhance the fusion of feature maps from different scales. Extensive experiments are conducted on three publicly available SAR ship datasets, DSSDD, SSDD, and HRSID, to validate the effectiveness of the proposed ESarDet. The experimental results demonstrate that ESarDet has distinct advantages over current state-of-the-art (SOTA) detectors in terms of detection accuracy, generalization capability, computational complexity, and detection speed.

A video tracking method based on spatial constrained coding (SCC) is proposed in this study. To characterise local image structure information, the dense scale-invariant feature transform (SIFT) descriptor is extracted for each pixel in the image. The proposed tracking method uses SCC model which adopts a new constrained strategy – weighted code, which is achieved by considering the sum of the weighted codes based on grey values of neighbouring pixels and distances between them. The proposed model is able to obtain robust code of corresponding pixels in the frames of complex scenes by taking spatial information into account, which enhances the stability of coding and makes the tracker more robust for object tracking. Twelve challenging sequences involving partial or full occlusion, large pose variation and drastic illumination change are chosen to test the proposed method. The experimental results show the proposed method performs excellent in comparison with other previously proposed trackers.

Face recognition has attracted enormous interest from researchers due to its widescale adaptation. However, most of the work in the literature is based on face recognition in controlled environments i.e., high-resolution face images with slight variation in pose, illumination and expression. CNN is used as backbone architecture in the face recognition models. Low-resolution face images having large variations in pose, illumination and expressions make a face recognition problem more challenging. It is difficult to extract discriminative features from low-resolution images. That's why the performance of face recognition models trained on high-resolution images deteriorate on low-resolution images. In this work, quality of low-resolution images is enhanced through multiple super-resolution GANs in order to improve the identification accuracy. Experiments are performed on SCface dataset and the improvement in the identification accuracy shows the effectiveness of using enhanced images generated through super-resolution GANs.

This work presents a method to automatically locate facial feature points under large variations in pose, illumination and facial expressions. First we propose a method to calculate probabilistic-like output for each pixel of image. This probabilistic-like output describes the possibility of the pixel to be the center of specified object. A Gaussian mixture model is used to approximate the distribution of probabilistic-like output. The centers of these Gaussians are assigned with a probabilistic-like measure and they are considered as candidate feature points. There might be one or more candidate feature points in each facial region. A 3D model of facial feature points is built to enforce constraints on the localization results of feature points. Compared with active shape model (ASM) and its variant methods, our method could accommodate larger variations in pose, lighting and face expressions. Moreover, it is less sensitive to initialization errors, accurate, and fast. It takes a computer with P4 CPU about 10 ms to locate the five feature points (two eye centers, two mouth corners and nose tip). The feature localization accuracy is comparable with the accuracy of manually labeled features and it is robust to noise (glasses, beards). Experiments on FERET gallery and PIE are reported in this paper as well.

Pose-invariant 3D face recognition using half face

Adaptive Neighbors Graph Learning for Large-Scale Data Clustering using Vector Quantization and Self-Regularization