Robust Visual Tracking Using Illumination Invariant Features in Adaptive Scale Model

The inverse synthetic aperture lidar (ISAL) have attracted increasing attention for its merits including small visual tracking which is considered as one of the important research topics in the field of computer vision due to its key role in versatile applications, such as precision guidance, intelligent video surveillance, human-computer interaction, robot navigation and public safety. The basic idea for implementing visual tracking is composed of finding the target object in a video or sequence of images, then determining its exact position in the next successive frames and finally generating the corresponding trajectory of this object. Visual tracking, however, is still a challenging problem in practice while taking into account the abrupt appearance changes of the target objects induced by their non-rigid transformation, the sophisticated lighting variation, the obstruction by the block or similar objects in the background and the camera jitter. Motivated by the successful applications in target detection and recognition in recent years, plenty of deep learning models have been integrated in the visual tracking and better performance over traditional methods was achieved in a series of data evaluations, which opens a new door in the field of visual tracking. In this paper, the overview and progress on visual tracking were summarized. The current challenges and corresponding solving approaches in this field are introduced firstly and in particular, several novel and mainstream visual tracking algorithms based on the deep learning are specially described and analyzed in details, including their basic ideas, advantages and disadvantages and future prospect.

In this paper, we propose a novel video watermarking scheme based on motion location. In the proposed scheme, independent component analysis is used to extract a dynamic frame from two successive frames of original video, and the motion is located by using the variance of 8 × 8 block in the extracted dynamic frame. Then according to the located motion, we choose a corresponding region in the former frame of the two successive frames, where watermark is embedded by using the quantization index modulation algorithm. The procedure above is repeated until each frame of the video (excluding the last one) is watermarked. The simulations show that the proposed scheme has a good performance to resist Gaussian noising, MPEG2 compression, frame dropping, frame cropping, etc.

Recent researches on visual tracking have shown significant improvement in accuracy by handling the large uncertainties induced by appearance variation and abrupt motion. Most studies concentrate on random walk based Markov chain Monte Carlo(MCMC) tracking methods which have shown inefficiency in sampling from complex and high-dimensional distributions. This paper proposes an adaptive Hamiltonian Monte Carlo sampling based tracking method within the Bayesian filtering framework. In order to suppress the random walk behavior in Gibbs sampling stage, the ordered over-relaxation method is used to draw the momentum item for the joint state variable. An adaptive step-size based scheme is used to simulate the Hamiltonian dynamics in order to reduce the simulation error and improve acceptance rate of the proposed samples. Furthermore, in designing the appearnce model, we introduce the locality sensitive histogram (LSH) to deal with appearance changes induced by illumination change. The proposed tracking method is compared with several state-of-the-art trackers using different quantitative measures: success rate and abruption capture rate. Extensive experimental results have shown its superiority to several other trackers.

Visual tracking is the process of locating, identifying, and determining of an object within video frames. From a Bayesian perspective, this is done by estimating the posterior density function. On the other hand, evolutionary optimization perspective would like to generate and select sufficiently optimize solution using two major components: diversification and intensification. This research will develop visual tracking algorithm using a Bayesian approach with evolutionary optimization in order to perform accurate tracking. The main idea is to combine Particle Markov Chain Monte Carlo (Particle-MCMC) as representation of Bayesian approach, with evolutionary optimization that is Particle Swarm Optimization (PSO) in each video frame. The visual tracking is regulated by Particle-MCMC filter algorithm and PSO will work within this filter to get more accurate tracking. Based on the dataset groundtruth, we found the accuracy of tracking can be increased considerably comparing to our previous research.

Video compression is an essential task in video storage and transmission. In this paper, we worked for compressing the video sequence using Self-organizing map (SOM) and then stored the output feature vector of SOM using Hopfield Neural Network. The SOM algorithm is based on unsupervised, competitive learning. It extracts the useful features from video frames by feature extraction method. In our approach, the video sequence is first divided into video frames. The frames are processed by passing to the input of Self-organizing map (SOM) that outputs the informative features by removing some of the redundant information contained in video frames. Self-organizing map is an efficient method for reducing the size of images in the neural network field. The feature vector obtained is further used for storing the patterns using Hopfield network. The feature vector is applied to the input of the Hopfield neural network and stored in the network for encoding the video frames. Thus the compressed video frames are encoded and stored in memory as a codeword. The codeword is used for reconstruction of the video frames by using the same Hopfield network. The codeword is applied to the Self-organizing map network and the video frames are reconstructed from those codeword. Simulation results show that high compression rate is achieved while maintaining the good reconstruction quality of images.

Real-time object tracking has wide applications in time-critical multimedia processing areas such as motion analysis and human–computer interaction. It remains a hard problem to balance between accuracy and speed. In this paper, we present a fast real-time context-based visual tracking algorithm with a new saliency prior context (SPC) model. Based on the probability formulation, the tracking problem is solved by sequentially maximizing the computed confidence map of target location in each video frame. To handle the various cases of feature distributions generated from different targets and their contexts, we exploit low-level features as well as fast spectral analysis for saliency to build a new prior context model. Then, based on this model and a spatial context model learned online, a confidence map is computed and the target location is estimated. In addition, under this framework, the tracking procedure can be accelerated by the fast Fourier transform. Therefore, the new method generally achieves a real-time running speed. Extensive experiments show that our tracking algorithm based on the proposed SPC model achieves real-time computation efficiency with overall best performance comparing with other state-of-the-art methods.

<div><p><em>Tracking of moving objects in video sequences are essential for many computer vision applications & it is considered as a challenging research issue due to dynamic changes in objects, shape, complex background, illumination changes and occlusion. Many traditional tracking algorithms fails to track the moving objects in real-time, this paper proposes a robust method to overcome the issue, based on the combination of particle filter and Principal Component Analysis (PCA), which predicts the position of the object in the image sequences using stable wavelet features, which in turn are extracted from multi scale 2-D discrete wavelet transform. Later, PCA approach is used to construct the effective subspace. Similarity degree between the object model and the prediction obtained from particle filter is used to update the feature vector to handle occlusion and complex background in video frames. Experimental results obtained from the proposed method are encouraging.</em></p></div>

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.

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, soft biometric trait classification has been receiving more attention in the computer vision community due to its wide range of possible application areas. Most approaches in the literature have focused on trait classification in controlled environments, due to the challenges presented by real-world environments, i.e. arbitrary facial expressions, arbitrary partial occlusions, arbitrary and nonuniform illumination conditions and arbitrary background clutter. In recent years, trait classification has started to be applied to real-world environments, with some success. However, the focus has been on estimation from single images or video frames, without leveraging the temporal information available in the entire video sequence. In addition, a fixed set of features are usually used for trait classification without any consideration of possible changes in the facial features due to head pose changes. In this paper, we propose a temporal, probabilistic framework first to robustly estimate continuous head pose angles from real-world videos, and then use this pose estimate to decide on the appropriate set of frames and features to use in a temporal fusion scheme for soft biometric trait classification. Experiments performed on large, real-world video sequences show that our head pose estimator outperforms the current state-of-the-art head pose approaches (by up to 51%), whereas our head pose conditioned biometric trait classifier (for the case of gender classification) outperforms the current state-of-the-art approaches (by up to 31%).

Persistent aerial video surveillance from small UAV (SUAV) platforms requires accurate and robust target tracking capabilities. However, video tracks can break due to excessive camera motion, target resolution, low signal-to noise ratio, video frame dropout, and frame-to-frame registration errors. Connecting broken tracks <i>(video track repair)</i> is thus essential for maintaining high quality target tracks. In this paper we present an approach to track repair based on multi-hypothesis sequential probability ratio tests (MHSPRT) that is suitable for real-time video tracking applications. To reduce computational complexity, the approach uses a target dynamics model whose state estimation covariance matrix has an analytic eigendecomposition. Chi-square gating is used to form feasible track-to-track associations, and a set of local hypothesis tests is defined for associating new tracks with coasted tracks. Evidence is accumulated across video frames by propagating posterior probabilities associated with each track repair hypothesis in the MHSPRT framework. Global maximum likelihood and maximum <i>a posteriori </i>estimation techniques resolve conflicts between local track association hypotheses. The approach also supports fusion of appearance-based features to augment statistical distributions of the track state and enhance performance during periods of kinematic ambiguity. First, an overview of the video tracker technology is presented. Next the track repair algorithm is described. Finally, numerical results are reported demonstrating performance on real video data acquired from an SUAV.

Visual object tracking is of a great application value in video monitoring systems. Recent work on video tracking has taken into account spatial relationship between the targeted object and its background. In this paper, the spatial relationship is combined with the temporal relationship between features on different video frames so that a real-time tracker is designed based on a hash algorithm with spatio-temporal cues. Different from most of the existing work on video tracking, which is regarded as a mechanism for image matching or image classification alone, we propose a hierarchical framework and conduct both matching and classification tasks to generate a coarse-to-fine tracking system. We develop a generative model under a modified particle filter with hash fingerprints for the coarse matching by the maximum a posteriori and a discriminative model for the fine classification by maximizing a confidence map based on a context model. The confidence map reveals the spatio-temporal dynamics of the target. Because hash fingerprint is merely a binary vector and the modified particle filter uses only a small number of particles, our tracker has a low computation cost. By conducting experiments on eight challenging video sequences from a public benchmark, we demonstrate that our tracker outperforms eight state-of-the-art trackers in terms of both accuracy and speed.

This paper presents a novel locality sensitive histogram algorithm for visual tracking. Unlike the conventional image histogram that counts the frequency of occurrences of each intensity value by adding ones to the corresponding bin, a locality sensitive histogram is computed at each pixel location and a floating-point value is added to the corresponding bin for each occurrence of an intensity value. The floating-point value declines exponentially with respect to the distance to the pixel location where the histogram is computed, thus every pixel is considered but those that are far away can be neglected due to the very small weights assigned. An efficient algorithm is proposed that enables the locality sensitive histograms to be computed in time linear in the image size and the number of bins. A robust tracking framework based on the locality sensitive histograms is proposed, which consists of two main components: a new feature for tracking that is robust to illumination changes and a novel multi-region tracking algorithm that runs in real time even with hundreds of regions. Extensive experiments demonstrate that the proposed tracking framework outperforms the state-of-the-art methods in challenging scenarios, especially when the illumination changes dramatically.

Visual object tracking aims to deliver accurate estimates about the state of the target in a sequence of images or video frames. Nevertheless, tracking algorithms are sensitive to different kinds of image perturbations that frequently cause tracking failures. Indeed, tracking failures result from the insertion of imprecise target-related data into the trackers’ appearance models, which leads the trackers to lose the target or drift away from it. Here, we propose a tracking fusion approach, which incorporates feedback and re-initialization mechanisms to improve overall tracking performance. Our fusion technique, called SymTE-TR, enhances trackers’ overall performance by updating their appearances models with reliable information of the target’s states, while resets the imprecise trackers. We evaluated our approach on a facial video dataset, which characterizes a particular challenging tracking application under different imaging conditions. The experimental results indicate that our approach contributes to enhancing individual tracker performances by providing stable results across the video sequences and, consequently, contributes to stable overall tracking fusion performances.

Mean shift tracker combined with online learning-based detector and Kalman filtering for real-time tracking