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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. 相似文献
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I&#x;l Celasun A.Murat Tekalp Mete H Gketekin Derin M Harmanc&#x; 《Signal Processing: Image Communication》2001,16(10):949-962
This paper integrates fully automatic video object segmentation and tracking including detection and assignment of uncovered regions in a 2-D mesh-based framework. Particular contributions of this work are (i) a novel video object segmentation method that is posed as a constrained maximum contrast path search problem along the edges of a 2-D triangular mesh, and (ii) a 2-D mesh-based uncovered region detection method along the object boundary as well as within the object. At the first frame, an optimal number of feature points are selected as nodes of a 2-D content-based mesh. These points are classified as moving (foreground) and stationary nodes based on multi-frame node motion analysis, yielding a coarse estimate of the foreground object boundary. Color differences across triangles near the coarse boundary are employed for a maximum contrast path search along the edges of the 2-D mesh to refine the boundary of the video object. Next, we propagate the refined boundary to the subsequent frame by using motion vectors of the node points to form the coarse boundary at the next frame. We detect occluded regions by using motion-compensated frame differences and range filtered edge maps. The boundaries of detected uncovered regions are then refined by using the search procedure. These regions are either appended to the foreground object or tracked as new objects. The segmentation procedure is re-initialized when unreliable motion vectors exceed a certain number. The proposed scheme is demonstrated on several video sequences. 相似文献
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In this work, we study the method exploiting natural language network to improve tracking performance. We propose a novel architecture which can combine class and visual information presented in tracking. To this end, we introduce a multimodal feature association network, allowing us to correlate the target class with its appearance during training and aid the localization of the target during inference. Specifically, we first utilize an appearance model to extract the target visual features, from which we obtain appearance cues, for instance shape and color. In order to employ target class information, we design a learned lightweight embedding network to embed the target class into a feature representation. The association network of our architecture contains a multimodal fusion module and a predictor module. The fusion module is used to combine features from class and appearance, yielding multimodal features with more expressive representations for the subsequent module. The predictor module is used to determine the target location in the current frame, from which we associate the class to the appearance. The class embedding module thus can learn appearance cues by exploiting the back-propagation functionality. To verify the abilities of our method, we select the official training and test splits of the LaSOT with annotated images and classes to perform experiments. In particular, we analyze the imbalance in the samples and employ a class validator discriminator to alleviate this problem. Extensive experimental results on LaSOT, UAV20L and UAV123@10fps demonstrate our method achieves competitive results while maintaining a considerable real-time speed. 相似文献
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针对传统人体跟踪方法中目标模型复杂、计算量大等问题,该文提出一种无目标模型的多层时空切片联合的人体跟踪算法.用多层时空切片中的多个动态区域表示人体,区域的选择无需使用任何预定义的目标区域模型.使用时空切片方法在图像序列空间中提取多层水平时空切片图像,在每层时空切片图像中,检测和跟踪潜在的运动区域,并根据区域运动一致性和空间一致性关系,将多个区域关联成不同的人体目标,实现多个人体目标的跟踪,从而将XYT 3维空间中的人体跟踪问题转化为多个XT 2维空间的区域联合跟踪问题.实验表明,该算法降低了跟踪的轨迹误差,满足实时性跟踪要求,同时通过多区域的联合增强了跟踪算法的抗干扰能力,即在人体部分区域丢失的情况下仍能有效跟踪. 相似文献
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序列图象中运动目标的检测 总被引：4，自引：0，他引：4
本文针对运动目标的特点，讨论了运动目标的几种检测方法，并对其中差分算法作了适当改进。经实验证明，改进的算法比原来算法能获得更令人满意的结果。 相似文献
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通过按一定几何规则排列特征点的方式,提出一种视觉标志,并针对该标识设计出相应的识别方案。跟踪阶段,在实时视频中建立一个“搜索窗口”,并提出一种简单的预测跟踪算法,对标志进行运动预测和跟踪,减小了搜索范围,提高了目标跟踪算法的实时性。 相似文献
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针对一般跟踪算法不能很好地解决航拍视频下目标分辨率低、视场大、视角变化多等特殊难点,该文提出一种融合目标显著性和在线学习干扰因子的无人机(UAV)跟踪算法.通用模型预训练的深层特征无法有效地识别航拍目标,该文跟踪算法能根据反向传播梯度识别每个卷积滤波器的重要性来更好地选择目标显著性特征,以此凸显航拍目标特性.另外充分利用连续视频丰富的上下文信息,通过引导目标外观模型与当前帧尽可能相似地来在线学习动态目标的干扰因子,从而实现可靠的自适应匹配跟踪.实验证明:该算法在跟踪难点更多的UAV123数据集上跟踪成功率和准确率分别比孪生网络基准算法高5.3%和3.6%,同时速度达到平均28.7帧/s,基本满足航拍目标跟踪准确性和实时性需求. 相似文献
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结合单双行人DPM模型的交通场景行人检测 总被引：1，自引：0，他引：1
针对日常交通场景下,行人目标易被遮挡,影响行人检测效果的问题,提出一种结合单行人和双行人DPM模型的交通场景行人检测方法.该方法首先从INRIA、ETH等行人数据集中提取训练样本的DPM特征,通过LatentSVM方法训练得到单、双人DPM模型;然后采用分类检测方法,将交通场景行人分为单独分布行人和混合分布行人两类.检测时首先使用双行人模型SDP-DPM对目标图像进行目标匹配,如果没有检测到双行人目标,则判定为单独分布行人情况,转而使用单行人模型SP-DPM进行检测,并保存检测结果;如果检测到双行人目标,则判定为混合分布行人情况,此时先保存对应的双行人滤波响应,再使用单行人模型进行二次检测,并将两次检测的结果进行加权结合.实验结果表明,本文算法能够在行人相互遮挡严重的交通环境下,有效检测出行人,整体精度优于传统的DPM算法和当前行人检测的主要流行算法. 相似文献
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本文在研究光流原理的基础上,采用光流计算和分析对运动目标进行了较为准确的跟踪。首先对图像进行预处理,包括图像的灰度化,阈值分割和边缘提取;其次通过改进的Lucas-Kanade光流法实现运动目标的检测;最后,求取目标特征点的重心和各点到重心的距离,通过设定合适的阈值,画出目标的跟踪矩形框,从而完成目标的跟踪。 相似文献
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The estimation of the velocity of objects imaged by television cameras is useful in different areas of image processing.The problem is solved by means of a linear estimation algorithm and the effects of noise superimposed to the signal are analyzed. The structure of a real-time estimator is then presented. Experimental results show that a very fine accuracy is obtained. They encourage its application to image coding for redundancy reduction using movement compensation. 相似文献
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在红外目标跟踪中,由于目标所处的背景信息复杂多变和目标外观的显著变化,单一的分类器不足以拟合多模态的数据。该文结合核相关滤波器(KCF)将多个核相关分类器通过集成学习整合到一个框架中。利用KCF分类器具有解析解的特点平衡跟踪鲁棒性与实时性之间的矛盾,从而解决单个分类器无法处理复杂背景与显著的外观变化问题,并显著提升目标跟踪的性能与稳定性。为了验证算法的有效性,该文利用两个核相关跟踪器联合学习出1个强分类器。大量的定性定量实验表明所提的算法的跟踪性能超过传统的KCF算法,且跟踪速度也超过大多数比较算法。 相似文献
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This letter presents a multi‐modal approach to tracking geographic objects such as buildings and road signs in a video sequence recorded from a moving vehicle. In the proposed approach, photogrammetric techniques are successfully combined with conventional tracking methods. More specifically, photogrammetry combined with positioning technologies is used to obtain 3‐D coordinates of chosen geographic objects, providing a search area for conventional feature trackers. In addition, we present an adaptive window decision scheme based on the distance between chosen objects and a moving vehicle. Experimental results are provided to show the robustness of the proposed approach. 相似文献
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标准Mean Shift跟踪算法仅能确定目标形心位置,而不能确定其旋转角,在跟踪细长形目标时鲁棒性不好。为此,该文提出了一种三自由度Mean Shift跟踪算法,新算法在计算目标特征分布直方图时,用像素的位置转角及其到目标形心的归一化距离加权,并将像素在局部坐标系下的特征转角作为新特征引入。这种新的目标表示模型能够方便地纳入Mean Shift优化框架,通过迭代求解,可同时精确确定目标的形心位置和方位指向。实验结果表明该算法精度高,计算量小。 相似文献
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摘 要：本文通过对跟踪目标颜色特征的分析,采用了基于颜色直方图的粒子滤波跟踪算法对目标进行实时跟踪。该算法首先确定目标跟踪模型并归一化颜色直方图,其次设定粒子初始化参数、计算粒子参数、比较颜色直方图从而确定目标位置,最后通过重采样更新粒子。该算法充分利用颜色直方图作为目标的描述特征,同时兼顾粒子滤波对复杂环境的要求,可以实现非线性和非高斯噪声系统的目标跟踪。仿真效果表明,该算法可以很好的对单目标和多目标进行实时跟踪。 相似文献
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Jiangxiong Fang Jie Yang Huaxiang LiuAuthor vitae 《AEUE-International Journal of Electronics and Communications》2011,65(11):915-923
Representing an object with multiple image fragments or patches for target tracking in a video has proved to be able to maintain the spatial information. The major challenges in visual tracking are effectiveness and robustness. In this paper, we propose an efficient and robust fragments-based multiple kernels tracking algorithm. Fusing the log-likelihood ratio image and morphological operation divides the object into some fragments, which can maintain the spatial information. By assigning each fragment to different weight, more robust target and candidate models are built. Applying adaptive scale selection and updating schema for the target model and the weighting factors of each fragment can improve tracking robustness. Upon these advantages, the novel tracking algorithm can provide more accurate performance and can be directly extended to a multiple object tracking system. 相似文献