Adaptive Hamiltonian MCMC sampling for robust visual tracking

When entering into the realm of Computer Vision, the first thing which comes in to mind is Visual tracking. Visual tracking by far comes into one of the most actively investigated research areas because of the fact that it has an extensive collection of applications in areas such as activity recognition, surveillance, motion analysis and as well as human computer interaction. Some serious challenges of this area which still create hindrance in achieving 100% accuracy are abrupt appearance and pose changes of an object along with its background blockage due to blockages called occlusion, illumination and lighting variances and changes in scale of target object in the frames. Moreover, diverse algorithms had been proposed for the resolution of said issue. Now in such cases, if we study the statistical analysis of correlation between two frames in a certain video, it can be efficiently utilized to get the most exact location of the targeted object. The algorithms in existence today do not completely exploit a strong spatio-temporal relationship that very often occurs between the two successive frames in a video sequence. Recent advances in correlation-based tracking systems have been proposed to address the problem in successive frames. In this thesis a very simple yet quite speedy and robust algorithm that in actual brings all the relevant information used for Visual Tracking. Two of the Models proposed are the “Locality Sensitive Histogram” and “Discriminative Scale Tracking Method”. These are robust enough to the variations which are based on appearance which are normally presented by blockage, pose, illumination and lighting variations alike. A scheme is proposed called scale adaptation which is very much clever to adapt variations of targeted scale in the most efficient manner. The Discriminative Scale Tracking Method is used for detection as well as scale change ultimately resulting in an effective tracking method in the end. Various different experiments with the best algorithms have demonstrated on challenging sequences that the suggested methodology attains promising results as far as robustness, accuracy, and speed is concerned.

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.

Object tracking using Langevin Monte Carlo particle filter and locality sensitive histogram based likelihood model

This paper presents a novel locality sensitive histogram (LSH) algorithm for visual tracking. Unlike the conventional image histogram that counts the frequency of occurrence of each intensity value by adding ones to the corresponding bin, an LSH 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 exponentially reduces with respect to the distance to the pixel location where the histogram is computed. An efficient algorithm is proposed that enables the LSHs to be computed in time linear in the image size and the number of bins. In addition, this efficient algorithm can be extended to exploit color images. A robust tracking framework based on the LSHs is proposed, which consists of two main components: a new feature for tracking that is robust to illumination change and a novel multiregion 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. Evaluation using the latest benchmark shows that our algorithm is the top performer.

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

Visual tracking has attracted a significant attention in the last few decades. The recent surge in the number of publications on tracking-related problems have made it almost impossible to follow the developments in the field. One of the reasons is that there is a lack of commonly accepted annotated data-sets and standardized evaluation protocols that would allow objective comparison of different tracking methods. To address this issue, the Visual Object Tracking (VOT) workshop was organized in conjunction with ICCV2013. Researchers from academia as well as industry were invited to participate in the first VOT2013 challenge which aimed at single-object visual trackers that do not apply pre-learned models of object appearance (model-free). Presented here is the VOT2013 benchmark dataset for evaluation of single-object visual trackers as well as the results obtained by the trackers competing in the challenge. In contrast to related attempts in tracker benchmarking, the dataset is labeled per-frame by visual attributes that indicate occlusion, illumination change, motion change, size change and camera motion, offering a more systematic comparison of the trackers. Furthermore, we have designed an automated system for performing and evaluating the experiments. We present the evaluation protocol of the VOT2013 challenge and the results of a comparison of 27 trackers on the benchmark dataset. The dataset, the evaluation tools and the tracker rankings are publicly available from the challenge website (http://votchallenge.net).

Although determining the space of entropic vectors for n random variables, denoted by Γ* <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> , is crucial for solving a large class of network information theory problems, there has been scant progress in explicitly characterizing Γ* <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> for n ≥ 4. In this paper, we present a certain characterization of quasi-uniform distributions that allows one to numerically stake out the entropic region via a random walk to any desired accuracy. When coupled with Monte Carlo Markov Chain (MCMC) methods, one may “bias” the random walk so as to maximize certain functions of the entropy vector. As an example, we look at maximizing the violation of the Ingleton inequality for four random variables and report a violation well in excess of what has been previously available in the literature. Inspired by the MCMC method, we also propose a framework for designing optimal nonlinear network codes via performing a random walk over certain truth tables. We show that the method can be decentralized and demonstrate its efficacy by applying it to the Vamos network and a certain storage problem from.

Human observers can correctly attribute changes in the appearance of a scene either to changes in the incident light or to changes in the spectral-reflectance properties of the scene. This ability was assessed as a function of the time course of illuminant and spectral-reflectance changes. Observers were presented with computer simulations of Mondrian patterns of 49 randomly selected Munsell papers. On each trial a Mondrian pattern was presented for 1 s; the pattern then changed either instantaneously or gradually into another Mondrian pattern, also presented for 1 s, which was related to the first either by an illuminant change or by an illuminant change accompanied by additional changes in the spectral-reflectance functions of the individual papers. Illuminant and spectral-reflectance change were applied linearly in time (with respect to CIE coordinates) over intervals ranging from 0 to 7 s. Observers indicated whether there was a spectral-reflectance change. They were able to make reliable discriminations between illuminant and spectral-reflectance changes both when the changes were applied instantaneously and when they were applied gradually over time, but performance worsened progressively as the duration of the changes increased, that is, as their rate decreased. It is suggested that discrimination in this task depends on the extraction of a low-level transient signal which is generated in response to rapid changes in scene appearance and which is progressively attenuated as changes occur more and more gradually.

Robust infrared target tracking based on particle filter with embedded saliency detection

Appearance change of moving targets is a challenging problem in visual tracking. In this paper, we present a novel visual object tracking algorithm based on the observation dependent hidden Markov model (OD-HMM) framework. The observation dependency is computed by structure complexity coefficients (SCC) which is defined to predict the target appearance change. Unlike conventional methods addressing the appearance change problem by investigating different online appearance models, we handle this problem by addressing the fundamental reason of motion -related appearance change during visual tracking. Based on the analysis of motion-related appearance change, we investigate the relationship between the structure of the object surface and the appearance stability. The appearance of complex structural regions is easier to change compared with that of smooth structural regions with object moving. Based on this, we define SCC to predict the appearance stability of moving objects. Different from the standard HMM-based tracking algorithms where observations between different frames are assumed to be independent, we consider the observation dependency between consecutive frames with the information provided by SCC. Moreover , we present a novel outlier removing method in appearance model updating which helps to avoid error accumulation. Experimental results on challenging video sequences demonstrate that the proposed visual tracking algorithm with OD-HMM and SCC achieves better performance than existing related tracking algorithms.

Co-trained generative and discriminative trackers with cascade particle filter

This paper concerns on overcoming the challenges caused by drastic appearance change in visual tracking, especially the long-term appearance variation due to occlusion or large object deformation. We aim to build a long-term appearance model for robust tracking against large appearance change in two new respects: using historical and distinguishing cues to model target representation and extracting effective spatial objectness features from each frame to distinguish outliers. For the first purpose, an adaptive superpixel-based appearance model is formulated. Different from previous superpixel-based trackers, a complementary feature set is defined for the update model to preserve the features of those temporally disappeared object parts especially under occlusion and large deformation. For the second purpose, three new spatial objectness cues specially designed for tracking are defined, including surrounding comparison, edge density change and weighted superpixel straddling. With these spatial objectness cues, our method facilitates target object localization and ensures the target has similar edge distribution between adjacent frames. These cues greatly improve the ability of our method to distinguish the target from its surrounding background. The adaptive appearance model retains valuable features of historical results, and the spatial objectness cues are extracted from the current frame, and thus they are finally combined to complement with each other to solve large appearance changes. The extensive evaluations on the CVPR 2013 online object tracking benchmark and VOT 2014 datasets demonstrate the effectiveness of our method as compared with related trackers.

We propose a new tracking method for an information providing system using LED traffic lights. The proposed tracking method is accomplished by comparing the correlation values of the packet header in each area of 4 division photodiode (PD). In this paper, to begin with, we show that the proposed tracking method can track the LED traffic lights better than the tracking method proposed previously for this system, and then, can also expand the service area. Next, we compare the performance among the proposed tracking schemes when the power of the ambient light is changed.

Visual tracking is an essential process for performing automated cell manipulation tasks. However, Real-time accurate cell tracking in FluidFM is challenging due to probe occlusion during manipulation. This study presents a correlation filtering-based tracking method for real-time, high-precision position tracking of cells and probes. In this method, a target template update strategy is proposed to realize cell tracking under probe occlusion, which abandons the use of pixel values in the occlusion area and reduces the interference of the occlusion area in model learning. Cell tracking and automated manipulation experiments were carried out to validate the proposed method. The Experimental results show that the proposed tracking method can accurately measure the positions of occlusion cells and probe. Furthermore, combined with the precise force control of the FluidFM’s probe, the automated cell Pick & Place experiments was accomplished with high accuracy and robustness.

On the asymptotic variance of reversible Markov chain without cycles