Adaptive Objectness for Object Tracking

There are mainly two components in almost every visual object tracking algorithm, which are the object presentation and the searching mechanism. In the literature, metaheuristic algorithms have been recently, widely used as searching method in visual tracking. In fact, the more discriminative is their combined object presentation the better is their precision. Salient background information is often inside the first detected region of the object, which may decrease considerably the tracking performance. In this work, we combine a powerful metaheuristic searching method, whale optimization algorithm (WOA), with the discriminative presentation named as corrected background weighted histogram (CBWH) to improve the tracking accuracy in difficult environments. WOA algorithm is used because of its strong searching ability, which enable it avoid being trapped in local optimum, and also its lowest computation cost compared to other metaheuristic algorithms. The WOA sensitivity parameters are studied experimentally to be adjusted in visual tracking. For a fair assessment of the proposed approach, its performance has been evaluated on 20 video sequences, from the famous object tracking benchmark OTB15, and compared with six (6) other state-of-art trackers. The evaluations were carried quantitatively and qualitatively on challenging situations and have provided satisfying results. The experimental results shows that WOA combined with CBWH can lead to further improvement, and better success rate in tracking.

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.

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.

Visual tracking of generic objects is one of the fundamental but challenging problems in computer vision. Here, we propose a novel fully convolutional Siamese network to solve visual tracking by directly predicting the target bounding box in an end-to-end manner. We first reformulate the visual tracking task as two subproblems: a classification problem for pixel category prediction and a regression task for object status estimation at this pixel. With this decomposition, we design a simple yet effective Siamese architecture based classification and regression framework, termed SiamCAR, which consists of two subnetworks: a Siamese subnetwork for feature extraction and a classification-regression subnetwork for direct bounding box prediction. Since the proposed framework is both proposal- and anchor-free, SiamCAR can avoid the tedious hyper-parameter tuning of anchors, considerably simplifying the training. To demonstrate that a much simpler tracking framework can achieve superior tracking results, we conduct extensive experiments and comparisons with state-of-the-art trackers on a few challenging benchmarks. Without bells and whistles, SiamCAR achieves leading performance with a real-time speed. Furthermore, the ablation study validates that the proposed framework is effective with various backbone networks, and can benefit from deeper networks. Code is available at https://github.com/ohhhyeahhh/SiamCAR.

Visual object tracking is a classical, but still open research problem in computer vision, with many real world applications. The problem is challenging due to several factors, such as illumination variation, occlusions, camera motion and appearance changes. Such problems can be alleviated by constructing robust, discriminative and computationally efficient visual features. Recently, biologically-inspired channel representations [9] have shown to provide promising results in many applications ranging from autonomous driving to visual tracking.

Visual object tracking for unmanned aerial vehicles (UAV) is widely used in many fields such as military reconnaissance, search and rescue work, film shooting, and so on. However, the performance of existing methods is still not very satisfactory due to some complex factors including viewpoint changing, background clutters and occlusion. The Siamese trackers, which offer a convenient way of formulating the visual tracking problem as a template matching process, have achieved success in recent visual tracking datasets. Unfortunately, these template match-based trackers cannot adapt well to frequent appearance change in UAV video datasets. To deal with this problem, this paper proposes a template-driven Siamese network (TDSiam), which consists of feature extraction subnetwork, feature fusion subnetwork and bounding box estimation subnetwork. Especially, a template library branch is proposed for the feature extraction subnetwork to adapt to the changeable appearance of the target. In addition, a feature aligned (FA) module is proposed as the core of feature fusion subnetwork, which can fuse information in the form of center alignment. More importantly, a method for occlusion detection is proposed to reduce the noise caused by occlusion. Experiments were conducted on two challenging benchmarks UAV123 and UAV20L, the results verified the more competitive performance of our proposed method compared to the existing algorithms.

As the development of Artificial Intelligent, visual object tracking plays a key role in computer vision area with numerous real-world applications. This paper proposes a novel approach with the combination of deep spatial transformer features and discriminative correlation filters-based tracking frameworks for the visual tracking problem. The deep spatial transformer features have several advantages compared to the standard deep features (fully connected layers). Firstly, they are more robust when object is suffering affine transformation because of the specific characteristic. Secondly, they have low dimensionality with deeper layer. Lastly, some structural information is contained during tracking procedure. We perform comprehensive experiments on two benchmark datasets: OTB and VOT2015. Surprisingly, compared with some traditional approach based on hand-crafted features, our results further show the affine robustness. We also maintain the accuracy and tracking speed compared with several discriminative correlation filter-based tracker at the same time. Finally, results comparable to state-of-the-art trackers are obtained on all two benchmark datasets.

Electrocardiogram (ECG) analysis is one of the most important approaches to cardiac arrhythmia detection. This paper proposed an ECG analysis approach with adaptive feature selection and support vector machines (SVMs). Many wavelet transform-based coefficients are used as candidates, but only a few coefficients are selected for classification problem of each class pair. In addition, the several variation classes are partitioned into two or more subclasses to improve the training efficiency of SVMs. The experimental results show that the proposed ECG analysis approach can obtain high recognition rate and reliable results

In this paper, we present a novel visual object tracking model via multi-view semi-supervised learning. Instead of concatenating multiple views into a single view directly to adapt to conventional machine learning algorithms, the combination of views is learned by exploiting the consensus of distinct views in the entire tracking. Besides, semi-supervised learning alleviates the lack of sufficient labeled samples in the tracking task, resulting in significant improvement in generalization performance. By showing that the sample data is block-circulant, we diagonalize it with the Discrete Fourier Transform to keep the tracking at high speed. Using features extracted by the VGG-19 network and in a 1:1 ratio of the labeled samples to the unlabeled, the experiment results on the CVPR2013 Online Object Tracking Benchmark show the effectiveness of our multi-view semi-supervised tracking model.

Sparse representation has been exploited for visual object tracking to yield impressive performance in recent years. However, sparse models in tracking are commonly limited by two intrinsic challenges, i.e., the high computational cost that makes it impractical in real-world applications and few reliable target observations available in dictionary initialization that renders it hard to handle great target dynamics in complicated scenarios. In this paper, we propose a novel object tracking method to address these problems. Our method integrates fast ensemble proposal and sparse verification, and the two complementarily work in the following way: The target proposals are fast generated and filtered to reduce computational load and provide evidence reference for later sparse verification, and the spare model updates its dictionary dynamically with the target observations verified by fused evidences. Extensive experimental results demonstrate that the proposed method handles the aforementioned problems with impressive success and that it outperforms state-of-the-art methods in various challenging scenarios.

Kim, J. and Kim, J., 2020. Estimation of water surface flow velocity using coastal video imagery by visual tracking with deep learning. In: Malvárez, G. and Navas, F. (eds.), Global Coastal Issues of 2020. Journal of Coastal Research, Special Issue No. 95, pp. 522-526. Coconut Creek (Florida), ISSN 0749-0208.This paper describes the method of flow velocity estimation of water surface in video imagery by tracking waves using deep neural network for visual object tracking with unsupervised learning. The model of deep neural network consists of two stages for scene separation and image registration to extract waves only and track the propagated waves, respectively. The dataset of video imagery acquired at Anmok beach of south Korea has been used to training the model and it learns the behavior of propagated waves. The performance of model is evaluated by measuring image similarity using test dataset. And the estimated flow velocity of water surface in propagated waves is compared with the flow from conventional image processing method of particle image velocity. The results show that our proposed approach with deep learning method is very promising to measure and predict coastal waves especially in the surf zone.

As a fundamental task in computer vision, visual object tracking has received much attention in recent years. Most studies focus on short-term visual tracking which addresses shorter videos and always-visible targets. However, long-term visual tracking is much closer to practical applications with more complicated challenges. There exists a longer duration such as minute-level or even hour-level in the long-term tracking task, and the task also needs to handle more frequent target disappearance and reappearance. In this paper, we provide a thorough review of long-term tracking, summarizing long-term tracking algorithms from two perspectives: framework architectures and utilization of intermediate tracking results. Then we provide a detailed description of existing benchmarks and corresponding evaluation protocols. Furthermore, we conduct extensive experiments and analyse the performance of trackers on six benchmarks: VOTLT2018, VOTLT2019 (2020/2021), OxUvA, LaSOT, TLP and the long-term subset of VTUAV-V. Finally, we discuss the future prospects from multiple perspectives, including algorithm design and benchmark construction. To our knowledge, this is the first comprehensive survey for long-term visual object tracking. The relevant content is available at https://github.com/wangdong-dut/Long-term-Visual-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).

In order to detect human operator performance degradation or breakdown, this paper proposes an adaptive support vector machine-based method to classify operator mental workload (MWL) into few discrete levels based on psychophysiological measures. Electroencephalogram, electrocardiogram, and electrooculography signals were recorded continuously while the operator was performing safety-critical process control operations in a simulated human-machine system. In coarse-grained analysis, the adaptive exponential smoothing (AES) technique is used to smooth the psychophysiological data and to remove strong artifacts without requiring templates. The MWL level is classified every 30 s by using bounded support vector machine (BSVM) and tenfold cross-validation techniques. Locality preservation projection (LPP) technique is utilized to derive salient psychophysiological features by means of feature reduction. By combining the AES-LPP and BSVM methods, the accuracy of the coarse-grained MWL classification was significantly improved by 11-13%. On the other hand, to perform MWL classification with higher temporal resolution and cross-subject and cross-trial generalizability, finer-grained data analysis is also conducted to recognize MWL levels every 5 s based on a combination of adaptive BSVM (ABSVM) and AES techniques. In comparison with the use of the BSVM algorithm alone, a significant performance improvement by 10-20% is achieved by using the AES-ABSVM method in the finer-grained MWL classification.

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