Object Tracking by Occlusion Detection via Structured Sparse Learning

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.

In this paper, we formulate particle filter-based object tracking as an exclusive sparse learning problem that exploits contextual information. To achieve this goal, we propose the context-aware exclusive sparse tracker (CEST) to model particle appearances as linear combinations of dictionary templates that are updated dynamically. Learning the representation of each particle is formulated as an exclusive sparse representation problem, where the overall dictionary is composed of multiple group dictionaries that can contain contextual information. With context, CEST is less prone to tracker drift. Interestingly, we show that the popular L1 tracker is a special case of our CEST formulation. The proposed learning problem is efficiently solved using an accelerated proximal gradient method that yields a sequence of closed form updates. To make the tracker much faster, we reduce the number of learning problems to be solved by using the dual problem to quickly and systematically rank and prune particles in each frame. We test our CEST tracker on challenging benchmark sequences that involve heavy occlusion, drastic illumination changes, and large pose variations. Experimental results show that CEST consistently outperforms state-of-the-art trackers.

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.

Research on object tracking has been an active field because of its fundamental roles in surveillance and monitoring. In this paper, a new adaptive algorithm for fast target tracking based on hierarchical block matching is proposed. The improvement of the algorithm lies in three aspects. First, the hierarchical pyramid method is applied to enhance the search efficiency of the local features. In addition, at different pyramid level, an adaptive searching strategy is adopted according to the number of candidate features. Second, since image blocks make full use of local characters of features, the structure similarity method, associated with the relation coefficient in statistics, can be exploited to design a new matching criteria, which provides more precise matching results, especially in the complex scenes such as the change of illumination and partial occlusion. Lastly, traditional algorithms often track the object through adjacent frames, whereas our algorithm will search two frames with a variable interval on the basis of matching results. The intervals are obtained by using the bisection method and thus the searching time can be greatly saved. Simulation results show that the proposed block matching method has better performances in the searching time and accuracy in contrast to the classic Mean-Shift target tracking algorithm and Particle Filter target tracking algorithm under the complex circumstances such as illumination variation and partial occlusion.

Object tracking in video is a challenging problem in several applications such as video surveillance, video compression, video retrieval, and video editing. Tracking an object in a video is not easy due to loss of information caused by illumination changing in a scene, occlusions with other objects, similar target appearances, and inaccurate tracker responses. In this paper, we present a novel object detection and tracking algorithm via structured output prediction classifier. Given an initial bounding box with its position, we first divide it into sub-blocks with a predefined size. Next, we extract the features from each sub-blocks with Haar-like features method. And then we learn those features with a structured output prediction classifier. We treat the sub-blocks obtained from the initial bounding box as positive samples and then randomly choose negative samples from search windows defined by the specific area around the bounding box. After that, we obtain prediction scores for each sub-blocks both from positive and negative samples. We construct a region-graph with sub-blocks as nodes and classifier’s score as weight to detect the target object in each frame. Our experimental results show that the proposed method outperforms state-of-the-art object tracking algorithms.KeywordsObject DetectionObject TrackingSupport Vector Machine

Video object segmentation and tracking are two essential building blocks of smart surveillance systems. However, there are several issues that need to be resolved. Threshold decision is a difficult problem for video object segmentation with a multi-background model. In addition, some conditions make robust video object tracking difficult. These conditions include nonrigid object motion, target appearance variations due to changes in illumination, and background clutter. In this paper, a video object segmentation and tracking framework is proposed for smart cameras in visual surveillance networks with two major contributions. First, we propose a robust threshold decision algorithm for video object segmentation with a multi-background model. Second, we propose a video object tracking framework based on a particle filter with the likelihood function composed of diffusion distance for measuring color histogram similarity and motion clue from video object segmentation. The proposed framework can track nonrigid moving objects under drastic changes in illumination and background clutter. Experimental results show that the presented algorithms perform well for several challenging sequences, and our proposed methods are effective for the aforementioned issues.

The Kalman filter has long been regarded as the optimal solution to many applications in computer vision for example the tracking objects, prediction and correction tasks. Its use in the analysis of visual motion has been documented frequently, we can use in computer vision and open cv in different applications in reality for example robotics, military image and video, medical applications, security in public and privacy society, etc. In this paper, we investigate the implementation of a Matlab code for a Kalman Filter using three algorithm for tracking and detection objects in video sequences (block-matching (Motion Estimation) and Camshift Meanshift (localization, detection and tracking object)). The Kalman filter is presented in three steps: prediction, estimation (correction) and update. The first step is a prediction for the parameters of the tracking and detection objects. The second step is a correction and estimation of the prediction parameters. The important application in Kalman filter is the localization and tracking mono-objects and multi-objects are given in results. This works presents the extension of an integrated modeling and simulation tool for the tracking and detection objects in computer vision described at different models of algorithms in implementation systems.

How to achieve a robust performance remains an intractable problem in the various object tracking algorithms due to some unfavorable factors, e.g. occlusions, appearance change, etc. In this paper, a robust object tracking approach is proposed based on feature fusion and occlusion detection. Under the relevant filtering model, two complementary features, HOG and color name features, are fused via a weighting strategy. Moreover, an occlusion detection method is presented according to the response function of the fused features. Experimental results on several challenging sequences demonstrate the effectiveness and feasibility of the proposed method.

Object detection and tracking under complex environment are challenging because of the disturbances induced by background clutter, illumination changes, occlusions and other factors. The bulk of traditional algorithms basically rely on hand‐crafted features, which are not sufficiently robust to a complex environment. Moreover, the processes of detection and tracking are separated, which leads to the overall efficiency not high. In this study, a novel local probability model (LPM)‐based mean shift (MS) algorithm is proposed to integrate object detection and tracking. The main contributions include: (i) a new framework based on the combination of LPM and MS is established for the integration of object tracking and detection. (ii) For object detection, the training and prediction of LPM are built by stacked denoising autoencoders based deep learning. (iii) For object tracking, an MS tracking algorithm leveraging LPM is modified to improve the tracking efficiency under a complex environment. Experimental results demonstrate that the proposed method is superior to the colour histograms based MS and histograms of oriented gradients based MS in terms of robustness and tracking accuracy.

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.

Video motion target tracking has been widely used in the field of sports. Content-based video search has a broad application prospect and attracts more and more researchers’ attention. In view of the shortcomings of moving object detection in motion video at present, a human motion recognition method based on correlation vector machine is proposed. Combined with the improved Gaussian mixture model of moving object detection and tracking method in motion video, moving object detection and tracking are carried out. The design of the system is realized. Firstly, it collects sports videos, considers all kinds of sports objects in sports videos, uses improved background update difference method to detect sports objects, then uses correlation vector machine to recognize human motion, and finally uses AdaBoost classifier to use it in multiple sports videos and sports objects. Carry out follow-up experiments. Results in this way, we can track the sports target in sports video accurately and quickly, and the real-time performance of sports target tracking is better than other tracking methods, which provides a new research tool for tracking sports target in sports video.

Detection and analysis of events from video sequences is probably one of the most important research issues in computer vision and pattern analysis society. Before, however, applying methods and tools for analyzing actions, behavior or events, we need to implement robust and reliable tracking algorithms able to automatically monitor the movements of many objects in the scene regardless of the complexity of the background, existence of occlusions and illumination changes. Despite the recent research efforts in the field of object tracking, the main limitation of most of the existing algorithms is that they are not enriched with automatic recovery strategies able to re-initialize tracking whenever its performance severely deteriorates. This is addressed in this paper by proposing an automatic tracking recovery tool which improves the performance of any tracking algorithm whenever the results are not acceptable. For the recovery, non-linear object modeling tools are used which probabilistically label image regions to object classes. The models are also time varying. The first property is implemented in our case using concepts from functional analysis which allow parametrization of any arbitrary non-linear function (with some restrictions on its continuity) as a finite series of known functional components but of unknown coefficients. The second property is addressed by proposing an innovative algorithm that optimally estimates the non-linear model at an upcoming time instance based on the current non-linear models that have been already approximated. The architecture is enhanced by a decision mechanism which permits verification of the time instances in which tracking recovery should take place. Experimental results on a set of different video sequences that present complex visual phenomena (full and partial occlusions, illumination variations, complex background, etc) are depicted to demonstrate the efficiency of the proposed scheme in proving tracking in very difficult visual content conditions. Additionally, criteria are proposed to objectively evaluate the tracking performance and compare it with other strategies.