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.     

Network in network based weakly supervised learning for visual tracking

One of the key limitations of the many existing visual tracking method is that they are built upon low-level visual features and have limited predictability power of data semantics. To effectively fill the semantic gap of visual data in visual tracking with little supervision, we propose a tracking method which constructs a robust object appearance model via learning and transferring mid-level image representations using a deep network, i.e., Network in Network (NIN). First, we design a simple yet effective method to transfer the mid-level features learned from NIN on the source tasks with large scale training data to the tracking tasks with limited training data. Then, to address the drifting problem, we simultaneously utilize the samples collected in the initial and most previous frames. Finally, a heuristic schema is used to judge whether updating the object appearance model or not. Extensive experiments show the robustness of our method.     

红外序列图像目标跟踪的自适应Kalman滤波方法

提出了一种用于动态序列图像目标跟踪的自适应Kalman滤波方法。该方法用函数估计的思想估计目标的当前运动模型,同时实时修改滤波器的统计模型,并将最小二乘支持向量机应用于对当前目标运动模型的估计。实验表明,此种改进的Kalman滤波器的算法在跟踪机动目标时具有良好的性能。     

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.     

基于多特征相关滤波的红外目标跟踪

为实现在复杂背景和多干扰条件下红外目标的稳 定跟踪,提出一种基于多特征相关滤波的红外目标 跟踪算法。首先综合考虑生物视觉关注特性及目标运动特性,提取目标区域的空间特征和 运动特征,进而融合一种改进的卷积特征,生成多特征权值函数;然后在传统 相关滤波的基础上,引入多特 征权值函数用以表征不同候选区域的重要程度,形成权值相关滤波的红外目标跟踪框架;最 终得到能够表 征目标位置的置信图,从而完成红外目标的鲁棒跟踪。在6组不同条件下红外视频序列上的 实验结果表明, 和经典目标跟踪算法相比,本文方法在复杂背景下的平均跟踪成功率提升15%左右,能够有 效应对相似虚 假目标、遮挡、背景辐射强度变化和探测器晃动等不良因素的影响,适用于复杂背景条件下 的红外目标跟踪。     

基于粒子滤波的机载目标跟踪系统设计

基于微小型机载成像跟踪系统设计思想及需求,设计并实现了以高性能的DSP芯片TMS320-DM642为核心处理器,结合可编程逻辑器件CPLD和FPGA的实时图像跟踪处理平台。平台采用基于粒子滤波的目标跟踪算法,实现对目标的实时跟踪。采用卡尔曼滤波器,提高了粒子的利用效率,在改进了算法实时性的同时解决了图像跟踪系统的延时性问题,提高了跟踪系统的稳定性。算法仿真结果表明,与传统相关匹配算法相比,基于粒子滤波的跟踪算法具有更好的鲁棒性和实时性,能满足机载成像跟踪系统实时图像跟踪的要求。     

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.     

Occlusion-robust online multi-object visual tracking using a GM-PHD filter with CNN-based re-identification

We propose a novel online multi-object visual tracker using a Gaussian mixture Probability Hypothesis Density (GM-PHD) filter and deep appearance learning. The GM-PHD filter has a linear complexity with the number of objects and observations while estimating the states and cardinality of time-varying number of objects, however, it is susceptible to miss-detections and does not include the identity of objects. We use visual-spatio-temporal information obtained from object bounding boxes and deeply learned appearance representations to perform estimates-to-tracks data association for target labeling as well as formulate an augmented likelihood and then integrate into the update step of the GM-PHD filter. We also employ additional unassigned tracks prediction after the data association step to overcome the susceptibility of the GM-PHD filter towards miss-detections caused by occlusion. Extensive evaluations on MOT16, MOT17 and HiEve benchmark data sets show that our tracker significantly outperforms several state-of-the-art trackers in terms of tracking accuracy and identification.     

Robust player detection and tracking in broadcast soccer video based on enhanced particle filter

It is significant to detect and track soccer players in broadcast sports video, which is helpful to analysis player activity and team tactics. However, it is challenging to efficiently detect and track soccer players with shots switched and noise caused by auditorium and billboards. And for multi-player tracking how to treat the increase or decrease of player are also difficult. In this paper, a robust player detection algorithm based on salient region detection and tracking based on enhanced particle filtering are proposed. Salient region detection is used to segment sports fields, and then soccer players are detected by edge detection combined with Otsu algorithm. For soccer players tracking, we use an enhanced particle filter which we improve the algorithm in sample and the likelihood function combing the color feature and edge feature. Experimental results show the proposed algorithm can quickly and accurately detect and track soccer players in broadcast video.     

Learning discriminative update adaptive spatial-temporal regularized correlation filter for RGB-T tracking

The RGB-T trackers based on correlation filter framework have been extensively investigated for that they can track targets more accurately in most complex scenes. However, the performance of these trackers is limited when facing some specific challenging scenarios, such as occlusion and background clutter. For different tracking targets, most of these trackers utilize fixed regularization constraint to build the filter model, which is obviously unreasonable to effectively present the appearance changes and characteristics of a specific target. In addition, they adopt a simple model update mechanism based on linear interpolation, which can easily lead to model degradation in challenging scenarios, resulting in tracker drift. To solve the above problems, we propose a novel adaptive spatial-temporal regularized correlation filter model to learn an appropriate regularization for achieving robust tracking and a relative peak discriminative method for model updating to avoid the model degradation. Besides, to make better integrate the unique advantages of the two modes and adapt the changing appearance of the target, an adaptive weighting ensemble scheme and a multi-scale search mechanism are adopted, respectively. To optimize the proposed model, we designed an efficient ADMM algorithm, which greatly improved the efficiency. Extensive experiments have been carried out on two available datasets, RGBT234 and RGBT210, and the experimental results indicate that the tracker proposed by us performs favorably in both accuracy and robustness against the state-of-the-art RGB-T trackers.     

Multi-frame co-saliency spatio-temporal regularization correlation filters for object tracking

The spatial regularization weight of the correlation filter is not related to the object content and the model degradation in the tracking process. To solve this problem, a new multi-frame co-saliency spatio-temporal regularization correlation filters (MCSRCF) is proposed for visual object tracking. To the best our knowledge, this is the first application of co-saliency regularization to CF-based tracking. In MCSRCF, grayscale features, directional gradient histogram (HOG) features and CNN features are extracted to improve the tracking precision of the tracker. Secondly, the three-dimensional spatial saliency and semantic saliency are introduced to obtain the initial weight of the spatial regularization with object content information. Then, the heterogeneous saliency fusion method is exploited to add a co-saliency spatial regularization term to the objective function to make the spatial penalty weight learn the change of the object region. In additional, the temporal saliency regularization is introduced to learn the information between adjacent frames, which reduces the overfitting effect caused by inaccurate samples. A variety of evaluations are conducted on public benchmarks, and the experimental results show that the proposed tracker achieves good robustness against many state-of-the-art trackers in various complex scenarios.     

一种新型多特征融合粒子滤波视觉跟踪算法

针对单一视觉信息在动态变化环境下描述目标不够充分、跟踪目标不够稳定的缺点,提出了一种基于粒子滤波框架的新型多特征融合的视觉跟踪算法。采用颜色和形状信息来描述运动模型,通过民主合成策略将两种信息融合在一起,使得跟踪算法能根据当前跟踪形势自适应调整两种信息的权重以期达到最佳的最大似然比,实现信息间的优势互补。在设计粒子滤波跟踪算法时,利用自适应信息融合策略构建似然模型,提高了粒子滤波跟踪算法在复杂场景下的稳健性。实验结果表明,多特征融合跟踪算法不仅能准确、高效地跟踪目标,而且对光照、姿态变化引起的目标表观变化具有良好的鲁棒性。     

强跟踪自适应滤波器实现机动目标的精确跟踪

机动目标状态估计中的一个主要问题是:目标运动的突变性导致状态噪声无法进行统计预测.传统的EKF将噪声看成是高斯白噪声有着本质上的不足,因而无法实现稳定的跟踪.引入Sage-Husa滤波算法对有色噪声进行在线的估计,一定程度上弥补了目标运动模型不够合理的缺憾.在此基础上,从系统容错设计基本原理出发,用归一化残差功率法实时地检测可能出现的数值发散现象,一旦检测到发散,印通过一种改进的强跟踪自适应滤波器进行抑制,有效地提升了滤波的健硕性,实现了稳定跟踪.最后,针对高机动目标的运动特性,仿真验证采用变维滤波模型,用EKF对目标的简单机动进行跟踪,只有目标运动突变时才采用本文提出的算法,以提升计算的实时性.仿真结果表明此算法对高机动目标的跟踪是有效的.     

基于核密度多伯努利视频多目标跟踪算法

针对复杂环境下数目变化、目标紧邻及尺寸变化的 视频多目标跟踪问题,在多伯努利滤波框架 下,提出一种自适应的变数目视频多目标跟踪算法。算法通过引入核密度背景减除技术,可 以有效抑制 背景干扰;然后融入连续自适应均值漂移(CAMShift)技术,并提出目标紧邻和尺寸变化处理 机制,可 以有效提高算法的自适应性;最后引入粒子标记技术,可以有效实现对视频多目标的轨迹跟 踪。对彩色视频和红外视频序列图像的测试结果表明,本文提出算法可以有效实现对复杂环 境下数目变化的视频多目标自适应跟踪,且具有较好的鲁棒性。     

基于自适应无迹粒子滤波的目标跟踪算法

为解决复杂场景中目标跟踪问题,提出了一种噪声未知情况下的自适应无迹粒子滤波(A-UPF)算法。算法采用改进的Sage-Husa估计器对系统未知噪声的统计特性进行实时估计和修正,并与无迹Kalman粒子滤波器相结合产生优选的建议分布函数,降低系统估计误差的同时有效提升了系统的抗噪声能力。实验结果表明,本文方法对于复杂条件下的目标跟踪问题具有较高的精度和较强的鲁棒性。     

基于DSP利用粒子滤波算法实现目标跟踪

以TMS320DM643数字信号处理器(DSP)为核心,构建了高性能的视频处理平台,并利用该硬件平台实现粒子滤波(PF)算法完成对目标的跟踪。介绍了视频处理平台的基本结构和核心器件,并阐述了DSP实现运动目标跟踪的软件设计。实验表明,该系统能够实时跟踪目标,且相对于传统的图像采集卡与计算机结合的处理系统,具有体积小、低功耗、模块化和移动性好的特点,容易集成到各种移动设备中实现自主导航、安防监控等功能。     

多特征融合的优化粒子滤波红外目标跟踪

针对粒子滤波重采样中粒子贫化问题,采用了权值选择的优化方法,对每个粒子的权值进行排序,选取其中权值较大的粒子参与跟踪估计,使权值较小的粒子有机会参与下一状态的估计,保证参与状态估计的大部分粒子具有多样性,有效克服粒子贫化现象。为了进一步提高跟踪性能,根据红外目标成像特点,融合目标梯度特征和灰度特征建立观测模型,并根据置信度实时调整每个特征对跟踪结果的影响,且自适应更新模板。经仿真验证,红外目标在复杂背景或遇到遮挡情况下,该算法能够精确鲁棒地跟踪目标。     

Real-time and robust visual tracking with scene-perceptual memory

Unmanned aerial vehicle (UAV) based aerial visual tracking is one of the research hotspots in computer vision. However, the mainstream trackers for UAV still have two shortcomings: (1) the accuracy of correlation filter tracker is greatly improved with more complex model, it impedes accuracy-speed trade-off. (2) object occlusion and camera motion in the aerial tracking scene also seriously restrict the application of aerial tracking. To address these problems, and inspired by AutoTrack tracker, we propose an aerial correlation filtering tracker based on scene-perceptual memory, Fast-AutoTrack. Firstly, to perceive and judge tracking anomalies, such as object occlusion and camera motion, inspired by the peak sidelobe ratio and AutoTrack, a confidence score is designed by perceiving and remembering the changing trend of the confidence and the local historical confidence. Secondly, after tracking anomaly occurring, several search regions are predicted based on the local object motion trend and the Spatio-temporal context information for object re-detection. Finally, to accelerate the model updating, the perceptual hashing algorithm (PHA) is used to obtain the similarity of the search regions between two adjacent frames. On typical aerial tracking datasets UAVDT, UAV123@10fps, and DTB70, Fast-AutoTrack run 71.4% faster than AutoTrack with almost equal accuracy and show favorable accuracy-speed trade-off.     

Sequence-tracker: Multiple object tracking with sequence features in severe occlusion scene

Multiple object tracking is one of the most fundamental tasks in computer vision, and it is still very challenging for real-world applications due to its severe occlusion and motion blur. Most of the existing methods solve these multiple object tracking issues by performing data association based on the deep features of the detections in consecutive frames, which only contain the spatial information of the detected objects. Therefore, the inaccuracy of data association would easily occur, especially in the severe occlusion scenes. In this paper, a novel multiple object tracking model named sequence-tracker (STracker) has been proposed, which combines both the temporal and spatial features to perform data association. We trained a sequence feature extraction network based on video pedestrian re-identification offline, fused the obtained sequence features with the depth features of the previous frame, and then implemented the Hungarian algorithm for data association. Experiments have been carried out to validate the effectiveness of the proposed algorithm and the corresponding results indicates that it can significantly improve the trajectory quality of our dataset in this paper. Remarkably, for the public detector results from MOT official website, the proposed algorithm can achieve up to 57.2% MOTA and 50.9% IDF1 on the MOT17 dataset.