Robust Object Tracking via Locality Sensitive Histograms

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.

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.

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.

Simulation of indoor space thermal energy circulation in architectural environment design detail features based on visual object tracking algorithm

We present an algorithm for robust and real-time visual tracking under challenging illumination conditions characterized by poor lighting as well as sudden and drastic changes in illumination. Robustness is achieved by adapting illumination-invariant binary descriptors to dense image alignment using the Lucas and Kanade algorithm. The proposed adaptation preserves the Hamming distance under least-squares minimization, thus preserving the photometric invariance properties of binary descriptors. Due to the compactness of the descriptor, the algorithm runs in excess of 400 fps on laptops and 100 fps on mobile devices.

This letter presents a novel visual tracking algorithm by using Incremental Non-negative Matrix Factorization (INMF) and dual ${\ell _1}$ -norm constraints. Firstly, we introduce one ${\ell _1}$ regularization into the NMF reconstruction, which enables appearance model to tolerate different noises to some extent. Meanwhile, we enforce another ${\ell _1}$ regularization on the projection coefficients when using iterative operators to obtain NMF basis vectors for the effective tracking. Secondly, to obtain the sparse error and projection coefficient matrice, we present an iterative algorithm to solve the optimal problem, which ensures the representation is more robust. Finally, we take partial occlusion into construct likelihood function, and combined with INMF learning to update appearance model for alleviating tracking drift. Experimental results compared with the state-of-the-art tracking methods demonstrate the proposed algorithm achieves favorable performance when the object undergoes large occlusion, motion blur and illumination changes.

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.

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.

In order to enhance the robustness of visual tracking algorithm in complex environment, a novel visual tracking algorithm based on multifeature selection and sparse representation is proposed. In the framework of particles filter, particles with low target similarity are first filtered out by a fast algorithm; then, based on the principle of sparsely reconstructing the sample label, the features with high differentiation against the background are involved in the computation so as to reduce the disturbance of occlusions and noises. Finally, candidate targets are linearly reconstructed via sparse representation and the sparse equation is solved by using APG method to obtain the state of the target. Four comparative experiments demonstrate that the proposed algorithm in this paper has effectively improved the robustness of the target tracking algorithm.

An efficient iterative Earth Mover's Distance (iEMD) algorithm for visual tracking is proposed in this paper. The Earth Mover's Distance (EMD) is used as the similarity measure to search for the optimal template candidates in feature-spatial space in a video sequence. The local sparse representation is used as the appearance model for the iEMD tracker. The maximum-alignment-pooling method is used for constructing a sparse coding histogram which reduces the computational complexity of the EMD optimization. The template update algorithm based on the EMD is also presented. When the camera is mounted on a moving robot, e.g., a flying quadcopter, the camera could experience a sudden and rapid motion leading to large inter-frame movements. To ensure that the tracking algorithm converges, a gyro-aided extension of the iEMD tracker is presented, where synchronized gyroscope information is utilized to compensate for the rotation of the camera. The iEMD algorithm's performance is evaluated using eight publicly available videos from the CVPR 2013 dataset. The performance of the iEMD algorithm is compared with eight state-of-the-art tracking algorithms based on relative percentage overlap. Experimental results show that the iEMD algorithm performs robustly in the presence of illumination variation and deformation. The robustness of this algorithm for large inter-frame displacements is also illustrated.

In order to effectively track high-speed objects in real time without any other instrumental methods but only based on computer vision, we propose an algorithm on camera control part by utilizing a history learning model in which a one-dimension Markov Transition Matrix synthesizes some historical information efficiently. The algorithm provides serials of conventional algorithms an effectively compensation when they perform worse in an interactive, high-speed circumstance, even lose the targets. Experimental results show that the new method is more stable and robust.KeywordsVisual trackingMarkov Chainmachine learningonline algorithms

COVID-19 is the repugnant but the most searched word since its outbreak in November 2019 across the globe. The world has to battle with it until an effective solution is developed. Due to the advancement in mobile and sensor technology, it is possible to come up with Internet of things-based healthcare systems. These novel healthcare systems can be proactive and preventive rather than traditional reactive healthcare systems. This article proposes a real-time IoT-enabled framework for the detection and prediction of COVID-19 suspects in early stages, by collecting symptomatic data and analyzing the nature of the virus in a better manner. The framework computes the presence of COVID-19 virus by mining the health parameters collected in real time from sensors and other IoT devices. The framework is comprised of four main components: user system or data collection center, data analytic center, diagnostic system, and cloud system. To point out and detect the COVID-19 suspected in real time, this work proposes the five machine learning techniques, namely support vector machine (SVM), decision tree, naïve Bayes, logistic regression, and neural network. In our proposed framework, the real and primary dataset collected from SKIMS, Srinagar, is used to validate our work. The experiment on the primary dataset was conducted using different machine learning techniques on selected symptoms. The efficiency of algorithms is calculated by computing the results of performance metrics such as accuracy, precision, recall, F1 score, root-mean-square error, and area under the curve score. The employed machine learning techniques have shown the accuracy of above 95% on the primary symptomatic data. Based on the experiment conducted, the proposed framework would be effective in the early identification and prediction of COVID-19 suspect realizing the nature of the disease in better way.

Current unmanned aerial vehicle (UAV) visual tracking algorithms are primarily limited with respect to: (i) the kind of size variation they can deal with, (ii) the implementation speed which hardly meets the real-time requirement. In this work, a real-time UAV tracking algorithm with powerful size estimation ability is proposed. Specifically, the overall tracking task is allocated to two 2D filters: (i) translation filter for location prediction in the space domain, (ii) size filter for scale and aspect ratio optimization in the size domain. Besides, an efficient two-stage re-detection strategy is introduced for long-term UAV tracking tasks. Large-scale experiments on four UAV benchmarks demonstrate the superiority of the presented method which has computation feasibility on a low-cost CPU.

CFGVF: An improved correlation filters based visual tracking algorithm

In recent years, applications of visual tracking algorithms has seen a substantial growth with deployments in intelligent robots such as drones for human tracking. The algorithms for such tasks has to be efficient in terms of computational cost while been robust, accurate and fast. Object tracking algorithms based on handcrafted heuristics and constraints are widely used in uav applications. The handcrafted heuristics are mostly implemented for task-oriented applications which limits the extensions in uav's capability beyond the predefined functions. This paper considers the challenges of tracking and landing an autonomous uav on a speed high moving target, and presents a visual tracking algorithm that integrates correlation filters with deep comparison network for real-time tracking with state-of-the-art accuracy. The method first tracks the target upto translation using an online learnt model via local search technique. The changes in scale is estimated by a deep comparison network (DCN) instead of the commonly used pyramidal approach. In a single network evaluation, DCN can estimate the changes in scale as well as compensate the drifting of the tracker by refining the object region estimated by the correlation filters. The network is end-to-end trained which attempts to learn a powerful matching function for object localization using a known template. Generally, the integrated framework can be viewed as coarse-to-fine level motion estimation. Moreover, the framework can redetect the lost target without a need for a separate detector.