Color Name Feature Research Articles

Moving Object Tracking in Satellite Videos by Kernelized Correlation Filter Based on Color-Name Features and Kalman Prediction

This paper studies moving object tracking in satellite videos. For the satellite videos, the object size in the images may be small, the object may be partly occluded, and the image may contain an area resembling dense objects. To handle the above problems, this paper puts forward a kernelized correlation filter based on the color-name feature and Kalman prediction. The original image is mapped to the color-name feature space so that the tracker can process the image with multichannel color features. The Kalman filter is used to predict the moving object position in the tracking process, and the detection area is determined according to the predicted position. The Kalman filter is updated with the detection results to improve the tracking accuracy. The proposed algorithm is tested on Jilin-1 datasets. Compared with the other seven tracking algorithms, the experiment results show that the proposed algorithm has stronger robustness for several complex situations such as rapid target motion and similar object interference. Besides, it is also shown that the proposed algorithm can prevent the problem of tracking failure when the moving object is partially occluded.

MFCFSiam: A Correlation-Filter-Guided Siamese Network with Multifeature for Visual Tracking

With the development of deep learning, trackers based on convolutional neural networks (CNNs) have made significant achievements in visual tracking over the years. The fully connected Siamese network (SiamFC) is a typical representation of those trackers. SiamFC designs a two-branch architecture of a CNN and models’ visual tracking as a general similarity-learning problem. However, the feature maps it uses for visual tracking are only from the last layer of the CNN. Those features contain high-level semantic information but lack sufficiently detailed texture information. This means that the SiamFC tracker tends to drift when there are other same-category objects or when the contrast between the target and the background is very low. Focusing on addressing this problem, we design a novel tracking algorithm that combines a correlation filter tracker and the SiamFC tracker into one framework. In this framework, the correlation filter tracker can use the Histograms of Oriented Gradients (HOG) and color name (CN) features to guide the SiamFC tracker. This framework also contains an evaluation criterion which we design to evaluate the tracking result of the two trackers. If this criterion finds the SiamFC tracker fails in some cases, our framework will use the tracking result from the correlation filter tracker to correct the SiamFC. In this way, the defects of SiamFC’s high-level semantic features are remedied by the HOG and CN features. So, our algorithm provides a framework which combines two trackers together and makes them complement each other in visual tracking. And to the best of our knowledge, our algorithm is also the first one which designs an evaluation criterion using correlation filter and zero padding to evaluate the tracking result. Comprehensive experiments are conducted on the Online Tracking Benchmark (OTB), Temple Color (TC128), Benchmark for UAV Tracking (UAV-123), and Visual Object Tracking (VOT) Benchmark. The results show that our algorithm achieves quite a competitive performance when compared with the baseline tracker and several other state-of-the-art trackers.

Research on Target Tracking Algorithm Based on context Multi-feature Fusion

In traditional target tracking algorithms, cosine window is often used to suppress the boundary effect and expand the search range when the target is located, which will reduce the available background information. Besides, when a single feature is used, the representation of the target appearance is not strong. When the challenge factors such as background clutter and occlusion occur to the target, the target will drift, which will reduce the accuracy of target tracking and even lead to tracking failure. To solve this problem, this paper proposes a context-based target tracking algorithm that fuses multiple features, using directional gradient histogram features and color name features as input features. To solve the occlusion problem, the occlusion mechanism of average peak correlation energy and maximum response fraction value is designed to judge the updating of the model. When both values are higher than a certain historical mean, the model is updated; Otherwise, stop updating. Finally, experiments show that the tracking algorithm proposed in this paper is more robust than some traditional correlation filtering algorithms.

Long-term real time object tracking based on multi-scale local correlation filtering and global re-detection

This paper investigates long-term visual object tracking which is a complex problem in computer vision community and big data analysis, due to the variation of the target and the surrounding environment. A novel tracking algorithm based on local correlation filtering and global keypoint matching is proposed to solve problems occurred during long-term tracking such as occlusion, target-losing, etc. The algorithm consists of two major components: (1) local object tracking module, and (2) global losing re-detection module. The local tracking module optimizes the conventional correlation filtering algorithm. Firstly, the Color Name feature is applied to increase the color sensitivity. Secondly, a scale traversal is employed to accommodate target scale changes. In the global losing re-detection module, the target losing judgment and global re-detection is realized by keypoint feature models of foreground and background. The proposed tracker achieves the 1st place in the VTB50 test set with 81.3% precision and 61.3% success rate, which outperforms other existing state-of-the-art trackers by over 10%. And it achieves the 2nd place in our Chasing-Car test set with a higher real-time performance 43.2 fps. The experimental results show that the proposed tracker has higher accuracy and robustness when dealing with situations like object deformation, occlusion and target-losing, etc.

Visual Tracking Algorithm Based on Color Name Histogram

Aimed at the problem that traditional histogram is sensitive to illumination changes in visual tracking, combined with the CN(Color Name) feature, we proposed a new feature(denotes CNH, Color Name Histogram) based on color name. Firstly, the method projected the original RGB image to CN space to obtain robust 11 feature layers. Then, we counted the each pixel numbers of feature layers. Finally, normalizing the amount of pixels in each layer. In addition, we adopted a feature adaptive fusion method to combine CNH and HOG(Histogram of Oriented Gradient). In order to prove validity of the proposed algorithm, we use Staple(Sum of Template And Pixel-wise Learners) algorithm frame to make a controlled trial. In contrast with the reference algorithms, the success of our algorithm increases by 1.5% and the precision increases by 1.7%. The results show that this method retains the advantages of traditional histogram which is insensitive to target deformation, but also enhances the robustness to illumination change.

Visual object tracking with discriminative correlation filtering and hybrid color feature

The technology of visual object tracking based on correlation filter has good accuracy and efficiency. However, it is still necessary to be study further on the appearance model of the target, the scale variation of the target and so on. This paper proposes a tracking algorithm based on discriminative correlation filtering and a hybrid color feature. The hybrid color feature is composed of two parts, which are compressed color name features and Histogram of Oriented Gradient features based on opponent color space. These two parts features above are extracted from the target patch, respectively. For the first part, color-name features are extracted from a target patch firstly, and then block-based compressed color-name features are extracted according to these color-name features. For the second part, opponent color features are extracted from the target patch firstly, and then HOG features are extracted from these opponent color features. At the basis of the hybrid color feature, two different discriminative correlation filters are used to estimate the translation and the scale of the target, respectively. Finally, extensive experiments show that the tracking algorithm with the hybrid color features of this paper outperforming several state-of-the-art tracking algorithms.

CFGVF: An improved correlation filters based visual tracking algorithm

Visual tracking is a challenging task in computer vision. Correlation filter (CF) based visual tracking algorithm has become an attractive tracking technique, while there are still some limitations. Existing CF-based tracking algorithms are vulnerable to the influence of the surrounding background, and the usage of fixed scale template may easily lead to tracking failure. To overcome the above limitations, we propose a CFs-based visual tracking algorithm, CFs with Gabor energy filter, variable-scale template and features fusion (CFGVF). In CFGVF, the Gabor energy filter is firstly adopted to preprocess every frame of the image sequence, which largely eliminates the influence of illumination variation. Then, the Gabor energy, Histogram of Oriented Gradient and color naming features are integrated to enhance the ability of dealing with significant appearance variations such as deformation and motion blur. Furthermore, we propose a variable-scale template method to estimate the scale of the target object. Finally, an online updating schema is adopted to reduce the interference of surrounding background and occlusion. Experimental results on the visual tracking benchmark dataset OOTB show that the performance and robustness of the CFGVF tracker outperform those of several state-of-the-art trackers.

Robust Scale Adaptive Visual Tracking with Correlation Filters

Visual tracking is a challenging task in computer vision due to various appearance changes of the target object. In recent years, correlation filter plays an important role in visual tracking and many state-of-the-art correlation filter based trackers are proposed in the literature. However, these trackers still have certain limitations. Most of existing trackers cannot well deal with scale variation, and they may easily drift to the background in the case of occlusion. To overcome the above problems, we propose a Correlation Filters based Scale Adaptive (CFSA) visual tracker. In the tracker, a modified EdgeBoxes generator, is proposed to generate high-quality candidate object proposals for tracking. The pool of generated candidate object proposals is adopted to estimate the position of the target object using a kernelized correlation filter based tracker with HOG and color naming features. In order to deal with changes in target scale, a scale estimation method is proposed by combining the water flow driven MBD (minimum barrier distance) algorithm with the estimated position. Furthermore, an online updating schema is adopted to reduce the interference of the surrounding background. Experimental results on two large benchmark datasets demonstrate that the CFSA tracker achieves favorable performance compared with the state-of-the-art trackers.

Person reidentification using deep foreground appearance modeling

Person reidentification is the process of matching individuals from images taken of them at different times and often with different cameras. To perform matching, most methods extract features from the entire image; however, this gives no consideration to the spatial context of the information present in the image. We propose using a convolutional neural network approach based on ResNet-50 to predict the foreground of an image: the parts with the head, torso, and limbs of a person. With this information, we use the LOMO and salient color name feature descriptors to extract features primarily from the foreground areas. In addition, we use a distance metric learning technique (XQDA), to calculate optimally weighted distances between the relevant features. We evaluate on the VIPeR, QMUL GRID, and CUHK03 data sets and compare our results against a linear foreground estimation method, and show competitive or better overall matching performance.

Kernel correlation filters for visual tracking with adaptive fusion of heterogeneous cues

Abstract Although the correlation filter-based trackers have achieved competitive results both on accuracy and robustness, the performance of trackers can still be improved because the most existing trackers either use a fixed scale or a sole filtering template to represent a target object. In this paper, to effectively handle the scale variation and the drifting problem, we propose a correlation filter-based tracker by adaptively fusing the heterogeneous cues. Firstly, to tackle the problems of the fixed template size, the scale of a target object is estimated from a set of possible scales. Secondly, an adaptive set of filtering templates is learned to alleviate the drifting problem by carefully selecting object candidates in different situations to jointly capture the target appearance variations. Finally, a variety of simple yet effective features (e.g., the HOG and color name features) are effectively integrated into the learning process of filters to further improve the discriminative power of the filters. Consequently, the proposed correlation filter-based tracker can simultaneous utilizes different types of cues to effectively estimate the target's location and scale while alleviating the drifting problem. We have done extensive experiments on the CVPR2013 tracking benchmark dataset with 50 challenging sequences. The proposed tracker successfully tracked the targets in about 90% videos and outperformed the state-of-the-art trackers.

A Joint Multi-Feature and Scale-Adaptive Correlation Filter Tracker

Traditional tracking-by-detection trackers may fail to track targets owing to interferences, such as deformation, occlusion, fast or irregular motion, and background clutter. To improve the process of feature extraction, sample training, and the performance of the traditional kernelized correlation filter (KCF), this paper proposes a new joint multi-feature and scale-adaptive correlation filter tracking algorithm based on histogram of oriented gradients (HOG) and color-naming features. The new algorithm is composed of a position correlation filter tracker and a scale correlation filter tracker. It first uses a mask matrix to cut the training samples, obtains a higher proportion of real samples, and then trains the two correlation filter trackers. One is a position tracker that uses HOG and color-naming features to train two correlation filters, and then combines the results of the two correlation filters to calculate the target position. The second tracker is used to identify the scale with the maximum response size to select the target scale. The proposed algorithm achieves a competitive performance compared with algorithms, such as KCF, compressive tracking, and circulant structure kernel. The results show that this algorithm achieves a better performance when faced with occlusion, deformation, background clutter, and fast or irregular motion, and is able to track changes in target scale.

Robust visual tracking via patch based kernel correlation filters with adaptive multiple feature ensemble

Both patch based and correlation filter-based tracking methods have achieved competitive results on accuracy and robustness, but there is still a large room to improve their overall performance if carefully dealing with the challenging factors in visual tracking. In this paper, we present a patch based tracker which adaptively integrates the kernel correlation filters with multiple effective features. To take full advantage of the useful information from different parts of the target, we train each template patch by kernel correlation filtering method, and adaptively set the weight of each patch for each particle in a particle filtering framework. Experiments illustrate that this scheme can effectively handle the occlusion problem. Moreover, the effective features including the HOG features and color name features are effectively integrated to learn the correlations between the target and the background, the candidate patches and template ones, which further boosts the overall performance. Extensive experimental results on the CVPR2013 tracking benchmark demonstrate that the proposed approach performs favorably against some representative state-of-the-art tracking algorithms.

Multi-feature tracking via adaptive weights

In this work, we present a novel online object tracking algorithm by using multi-feature channels with adaptive weights. Firstly, we exploit intensity, histogram of gradient (HOG) and color naming features to generate a set of confidence maps where the confidence value of each pixel indicates the probability that this pixel belongs to the tracked object. The intensity feature covers the energy information and HOG feature depicts the texture information of the tracked object and its surrounding background respectively. Color naming features aforementioned not only provide high-level features to build a more stable appearance model, but also handle tracking with cluttered coloring background effectively. Secondly, we learn an online model that denotes the close relationship between the center of target and background context, which represents some statistical correlation in consecutive frames. Finally, we exploit the appearance model and online model to generate a confidence map for each feature channel, and then obtain a final confidence map by fusing confidence maps from different channels in an adaptive manner. The optimal location of the tracked object can be determined based on the maximum value in the fused final confidence map. Both qualitative and quantitative evaluations on the recent benchmark dataset demonstrate that the proposed tracking algorithm achieves more favorable performance than several state-of-the-art methods, especially for the color sequences.