Rotation-aware correlation filters for robust visual tracking

Recent years have witnessed several modified discriminative correlation filter (DCF) models exhibiting excellent performance in visual tracking. A fundamental drawback to these methods is that rotation of the target is not well addressed which leads to model deterioration. In this paper, we propose a novel rotation-aware correlation filter to address the issue. Specifically, samples used for training of the modified DCF model are rectified when rotation occurs, rotation angle is effectively calculated using phase correlation after transforming the search patch from Cartesian coordinates to the Log-polar coordinates, and an adaptive selection mechanism is further adopted to choose between a rectified target patch and a rectangular patch. Moreover, we extend the proposed approach for robust tracking by introducing a simple yet effective Kalman filter prediction strategy. Extensive experiments on five standard benchmarks show that the proposed method achieves superior performance against state-of-the-art methods while running in real-time on single CPU.     

一种基于深度学习的公路巡检无人机实时目标跟踪算法

公路中心标线的实时跟踪是公路巡检无人机视觉飞行中关键的一环.针对目前主流目标跟踪算法实时性差的问题,提出一种基于改进YOLO(you only look once)v3和Deep-SORT(deep simple online real-time tracking)的目标跟踪模型用于公路巡检无人机自主视觉飞行.通过引入...     

End-to-end DeepNCC framework for robust visual tracking

In this paper, we propose an NCC-based object tracking deep framework, which can be well initialized with the limited target samples in the first frame. The proposed framework contains a pretrained model, online feature fine-tuning layers and tracking processes. The pretrained model provides rich feature representations while online feature fine-tuning layers select discriminative and generic features for the tracked object. We choose normalized cross-correlation as a template tracking layer to perform the tracking process. To enable the learned features representation closely coordinated to the tracked target, we jointly train the feature representation network and tracking processes. In online tracking, an adaptive template and a fixed template are fused to find the optimal tracking results. Scale estimation and a high-confidence model update scheme are perfectly integrated into the framework to adapt to the target appearance changes. The extensive experiments demonstrate that the proposed tracker achieves superior performance compared with other state-of-the-art trackers.     

Online semi-supervised compressive coding for robust visual tracking

In this paper we propose an online semi-supervised compressive coding algorithm, termed SCC, for robust visual tracking. The first contribution of this work is a novel adaptive compressive sensing based appearance model, which adopts the weighted random projection to exploit both local and discriminative information of the object. The second contribution is a semi-supervised coding technique for online sample labeling, which iteratively updates the distributions of positive and negative samples during tracking. Under such a circumstance, the pseudo-labels of unlabeled samples from the current frame are predicted according to the local smoothness regularizer and the similarity between the prior and the current model. To effectively track the object, a discriminative classifier is online updated by using the unlabeled samples with pseudo-labels in the weighted compressed domain. Experimental results demonstrate that our proposed algorithm outperforms the state-of-the-art tracking methods on challenging video sequences.     

Combining passive visual cameras and active IMU sensors for persistent pedestrian tracking

Vision based pedestrian tracking becomes a hard problem when long-term/heavy occlusion happens or pedestrian temporarily moves out of the visual field. In this paper, a novel persistent pedestrian tracking system is presented which combines visual signal from surveillance cameras and sensor signals from Inertial Measurement Unit (IMU) carried by pedestrians themselves. IMU tracking performs Dead Reckoning (DR) approach utilizing accelerometer, gyroscope and magnetometer. IMU tracking has nothing to do with visual occlusion, so it keeps working even when pedestrians are visually occluded. Meanwhile, visual tracking assists in calibrating IMU to avoid the bias drift during DR. The experimental results show that the IMU and visual tracking are complementary to each other and their combination performs robust pedestrian tracking in many challenging scenarios.     

Re-identification framework for long term visual object tracking based on object detection and classification

In this paper, we address the problem of long-term visual object tracking and we present an efficient real-time single object tracking system suitable for integration in autonomous platforms that need to encompass intelligent capabilities. We propose a novel long-term tracking framework for classification based re-detection and tracking, that incorporates state estimation, object re-identification and automated management of tracking and detection results. Our method integrates a novel object re-identification technique which efficiently filters a number of detection candidates and systematically corrects the tracking results. Through extensive experimental validation on the UAV123, UAV20L and TLP datasets, we demonstrate the effectiveness of the proposed system and its advantage over several state-of-the art trackers. The results furthermore highlight the proposed tracker’s ability to handle challenges arising from real-world and long-term scenarios, such as variations in pose, scale, occlusions and out-of-view situations. Furthermore, we propose a variant that is suitable for deployment on autonomous robots, such as Unmanned Aerial Vehicles.     

Adaptive model updating for robust object tracking

In this paper, we exploit features extracted from convolutional neural network (CNN) to be better utilized for visual tracking. It is observed that CNN features in higher levels provide semantic information which is robust to appearance variations. Thus we integrate the hierarchical features in different layers of a deep model to correlation filter tracking framework. More specifically, correlation filters are learned on each layer to encode the object appearance. The peak-to-sidelobe ratio (PSR) is employed to measure the differences between image patches. To leverage the robustness of our model, we develop an adaptive model updating scheme to train the correlation filters according to different response maps. Extensive experimental results on three large scale benchmark datasets show that the proposed algorithm performs favorably against state-of-the-art methods.     

Deformable part-based tracking by coupled global and local correlation filters

Correlation filters have recently attracted attention in visual tracking due to their efficiency and high performance. However, their application to long-term tracking is somewhat limited since these trackers are not equipped with mechanisms to cope with challenging cases like partial occlusion, deformation or scale changes. In this paper, we propose a deformable part-based correlation filter tracking approach which depends on coupled interactions between a global filter and several part filters. Specifically, local filters provide an initial estimate, which is then used by the global filter as a reference to determine the final result. Then, the global filter provides a feedback to the part filters regarding their updates and the related deformation parameters. In this way, our proposed collaborative model handles not only partial occlusion but also scale changes. Experiments on two large public benchmark datasets demonstrate that our approach gives significantly better results compared with the state-of-the-art trackers.     

具有非高斯相关噪声的目标跟踪

为实现强杂波背景下视频的鲁棒跟踪,在常 用非线性系统模型的基础上引入柯 西高斯混合噪声模型,充分考虑了非高斯噪声前 后时刻的状态相关性,并以权重条件最小方差为标准,推导了非高斯相关噪声的最优建议分 布 函数,在粒子滤波框架内实现了非高斯相关噪声模型时系统状态的准确估计。在新算法的框 架内采用多特征自适应融合的方法,实现了强噪声背景下视频目标的鲁棒跟踪。实验结果表 明,本文方法扩展了粒子滤波的适用范围,有效提升了强噪声环境下视频目标跟踪的精度和 稳定性。     

Correlation filter via random-projection based CNNs features combination for visual tracking

Object tracking based on the Convolutional Neural Networks (CNNs) with multiple feature correlation filter (CF) has become one of the best object tracking frameworks. In this paper, we propose a novel approach of CNNs based CF, which combines deep features from CNNs into low-dimensional features. To achieve the dimensionality reduction, random-projection is used due to its data-independence and superior computational efficiency over other widely used. In our proposed approach, the spectral graph theory is applied to generate a random projection matrix. This method bypasses the time-consuming Gram–Schmidt orthogonalization, where the dimension of the feature is high. The combined features have very low dimensions, less than one tenth of the dimensions of the original deep features from CNNs, offering an improvement of tracking speed and without loss of performance simultaneously. Extensive experiments are conducted on large-scale benchmark datasets. The results demonstrate that the proposed algorithm outperforms the state-of-the-art methods.     

Attention meets involution in visual tracking

In visual tracking, both convolution and attention are widely employed for feature enhancement and fusion. However, convolution does not adequately model global dependencies of samples due to its operation on local neighbors, while attention gives too much attention to global dependencies and too little to local dependencies. It is intrinsically infeasible to combine both methods to integrate global and local information. However, a recently-proposed model called involution uses kernels differing in spatial extent but sharing across channels, making it possible to take advantage of both convolution and attention. We propose an attention-involution (Att-Inv) model that uses an attention mechanism to generate involution kernels to take both global and local dependencies of samples into account. To improve the performance of our tracker, we develop and implement strategies of backbone network modification, template updates, and regression of bounding box distributions. We evaluate our tracker using benchmarks such as GOT10k, LaSOT, TrackingNet and OxUvA. Experimental results show that it is competitive with state-of-the-art trackers.     

Robust ambiguous target handling for visual object tracking

This paper presents a robust method of handling ambiguous targets (partial occlusion, split region or mixed state of the partial occlusion and the split region) for visual object tracking. The object model is a combination of bounding box features and expected object region. These object properties are very compact and allow us to track objects in a cluttered environment. The target state is classified in the first stage by a state classifier. The state classifier is defined from a weighted cross-correlation of normalized area and normalized distance which are defined from the comparison of background model- and the motion-based object detections. The correlation can categorize the target state by using the overlap quantity of the detected objects from the both object detections. If the target is merged state (partial occlusion), we will identify and track each object in the merged region by the bounding box features. If the targets are the split region, these regions are identified and grouped by the expected object region. If the target is the mixed state, we use the methods for handling the split and the merged region. Finally, experimental results show that the proposed method can deal with tracking in cluttered environments.     

基于人工蜂群算法的粒子滤波目标跟踪

针对基于粒子滤波的视频目标跟踪算法中由于粒子重采样过程而导致粒子贫化的问题,提出了一种基于人工蜂群算法的粒子滤波目标跟踪算法,利用群体智能的特点使得粒子集在重采样前得到优化,保持了粒子的多样性,从而解决了粒子贫化问题,同时增加了有效粒子的数目.实验结果表明,基于人工蜂群算法的粒子滤波跟踪算法,比标准粒子滤波跟踪算法所需粒子数更少,对目标遮挡、较复杂背景有较好的跟踪效果.     

Accurate bounding-box regression with distance-IoU loss for visual tracking

Most existing trackers are based on using a classifier and multi-scale estimation to estimate the target state. Consequently, and as expected, trackers have become more stable while tracking accuracy has stagnated. While trackers adopt a maximum overlap method based on an intersection-over-union (IoU) loss to mitigate this problem, there are defects in the IoU loss itself, that make it impossible to continue to optimize the objective function when a given bounding box is completely contained within/without another bounding box; this makes it very challenging to accurately estimate the target state. Accordingly, in this paper, we address the above-mentioned problem by proposing a novel tracking method based on a distance-IoU (DIoU) loss, such that the proposed tracker consists of target estimation and target classification. The target estimation part is trained to predict the DIoU score between the target ground-truth bounding-box and the estimated bounding-box. The DIoU loss can maintain the advantage provided by the IoU loss while minimizing the distance between the center points of two bounding boxes, thereby making the target estimation more accurate. Moreover, we introduce a classification part that is trained online and optimized with a Conjugate-Gradient-based strategy to guarantee real-time tracking speed. Comprehensive experimental results demonstrate that the proposed method achieves competitive tracking accuracy when compared to state-of-the-art trackers while with a real-time tracking speed.     

基于二次加权Mean-Shift的目标实时跟踪

针对经典Mean-Shift跟踪算法需要多次迭代才能达到收敛的缺点,提出一种高效的Mean-Shift跟踪算法。在使用颜色空间作为目标特征的跟踪系统中,目标本身往往可以表征为区别于背景的颜色特征,而颜色特征的分布则与偏移向量的权值相对应。通过分析跟踪算法中不同的权值对收敛速度的影响,对加权系数进行了二次加权,使改进的算法只需要一次粗定位和一次精确定位2次迭代便可准确地对目标进行定位。试验结果表明,该算法在保证了经典算法准确性的同时,大大加快了向目标收敛的速度。     

TRBACF: Learning temporal regularized correlation filters for high performance online visual object tracking

Correlation filter-based trackers (CFTs) have recently shown remarkable performance in the field of visual object tracking. The advantage of these trackers originates from their ability to convert time-domain calculations into frequency domain calculations. However, a significant problem of these CFTs is that the model is insufficiently robust when the tracking scenarios are too complicated, meaning that the ideal tracking performance cannot be acquired. Recent work has attempted to resolve this problem by reducing the boundary effects from modeling the foreground and background of the object target effectively (e.g., CFLB, BACF, and CACF). Although these methods have demonstrated reasonable performance, they are often affected by occlusion, deformation, scale variation, and other challenging scenes. In this study, considering the relationship between the current frame and the previous frame of a moving object target in a time series, we propose a temporal regularization strategy to improve the BACF tracker (denoted as TRBACF), a typical representative of the aforementioned trackers. The TRBACF tracker can efficiently adjust the model to adapt the change of the tracking scenes, thereby enhancing its robustness and accuracy. Moreover, the objective function of our TRBACF tracker can be solved by an improved alternating direction method of multipliers, which can speed up the calculation in the Fourier domain. Extensive experimental results demonstrate that the proposed TRBACF tracker achieves competitive tracking performance compared with state-of-the-art trackers.     

Multi-target visual tracking and occlusion detection by combining Bhattacharyya Coefficient and Kalman filter innovation

This paper introduces an approach for visual tracking of multi-target with occlusion occurrence. Based on the author’s previous work in which the Overlap Coefficient (OC) is used to detect the occlusion, in this paper a method of combining Bhattacharyya Coefficient (BC) and Kalman filter innovation term is proposed as the criteria for jointly detecting the occlusion occurrence. Fragmentation of target is introduced in order to closely monitor the occlusion development. In the course of occlusion, the Kalman predictor is applied to determine the location of the occluded target, and the criterion for checking the re-appearance of the occluded target is also presented. The proposed approach is put to test on a standard video sequence, suggesting the satisfactory performance in multi-target tracking.     

具有遮挡鲁棒性的目标中心距离加权的跟踪算法

本文提出了一种实时跟踪算法,以目标中心距离加权的目标图像直方图作为模板,采用mean-shift迭代方法进行目标定位;当目标被部分遮挡时,用"分块匹配"的方法提高算法鲁棒性;对于超过一定像素的较大目标,本算法进行"降采样",大大减小运算量,从而实现了对大尺度目标的实时跟踪.实时视频流的实际跟踪系统验证了该方法的有效性.