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利用感知模型的长期目标跟踪

张博,江沸菠,刘刚(长沙师范学院信息科学与工程学院, 长沙 410100;湖南师范大学信息科学与工程学院, 长沙 410081;长沙师范学院信息科学与工程学院, 长沙 410100;中南大学物理与电子学院, 长沙 410083)

摘 要
目的 传统相关滤波目标跟踪算法存在两个问题,其一,使用循环移位产生的虚假负样本训练分类器,导致分类器分类能力受到限制;其二,当目标被严重遮挡时,由遮挡引起的一些不正确的样本(预测的目标图像)用于更新分类器,随着遮挡时间的增加,分类器将包含较多噪声信息并逐渐失去判别力,使得跟踪失败。针对上述问题,提出一种基于感知模型的长期目标跟踪算法,通过引入背景感知策略解决传统相关滤波器缺乏真实负样本问题,通过引入遮挡感知策略来有效跟踪被遮挡的目标。方法 首先,所提算法通过扩大采样区域,增加所产生训练样本数量,并引入裁剪矩阵,裁取移位后的样本以获得完整有效的样本,同时克服了由循环移位产生样本导致的边界效应问题;然后,利用无遮挡情况下一定帧数目标图像各自对应的分类器构建分类池;最后,在严重遮挡情况下利用最小化能量函数从分类池中选择最佳分类器进行重检测,以实现长期目标跟踪。结果 使用公开数据集对所提算法进行性能评估,结果表明,所提算法成功率为0.990,精确度为0.988。其较背景感知相关滤波(BACF)算法分别提升2.7%和2.5%。结论 所提算法在目标被遮挡、形变、尺度变化以及复杂背景下仍能较准确跟踪目标,具备较高的精确度和鲁棒性。
关键词
Long-term target tracking based on perceptual model

Zhang Bo,Jiang Feibo,Liu Gang(College of Information Science and Engineering, Changsha Normal University, Changsha 410100, China;College of Information Science and Engineering, Hunan Normal University, Changsha 410081, China;College of Information Science and Engineering, Changsha Normal University, Changsha 410100, China;College of Physical Science and Electronics, Central South University, Changsha 410083, China)

Abstract
Objective Visual target tracking is an important issue in machine vision. Its core tasks are to locate the target in a continuous video sequence and estimate the target’s motion trajectory. This method has been widely used in many fields, such as human-computer interaction, security monitoring, automatic driving, navigation, and positioning. Through extensive research by domestic and foreign experts in recent years, visual target-tracking technology has gradually matured. However, tracking targets accurately in complex scenes, such as intense illumination change, occlusion, deformation, scale change, and background clutter, remains a challenging task. Visual target-tracking algorithms can be divided into two categories, namely, generative and discriminative tracking methods. Generative tracking converts the tracking problem into the nearest neighbor search task of the target model, constructs the target model by using a template or sparse representation in the subspace, and achieves target tracking by searching for the most similar region in the target model. Discriminant tracking treats the tracking problem as a binary classification problem.The target is separated from the background by training the classifier to achieve target tracking. Given that the generated visual target-tracking algorithm needs to construct a complex target appearance model, its computational complexity is high, and its algorithm has poor real-time performance. Discriminant tracking algorithm uses samples of the target and surrounding background to train a classifier online and achieves target tracking by detecting and tracking. Its classifier obtains considerable background information during training. Thus, this method can distinguish foreground and background better and its performance is generally better than that of the generative tracking method. Correlation-filtering algorithm is an algorithm with better performance than discriminant tracking algorithm. The traditional correlation-filtering algorithm introduces the concept of dense sampling and uses cyclically shifted samples of the base samples as training samples, which greatly improve the classification ability of the filter. The introduction of kernel strategy maps the linear regression problem of the ridge to the nonlinear space and uses the discrete Fourier transform to transform the time-domain calculation into the frequency-domain calculation, which greatly reduces algorithm complexity. Although traditional correlation-filtering algorithm has many advantages, it also has shortcomings.Method First, this algorithm uses false negative samples generated by the cyclic shift to train a classifier, which limits the classifier’s classification ability. Second, several incorrect samples (predicted target images) caused by occlusion are used to update the classifier when the target is seriously occluded. With an increase in occlusion time, the classifier will contain considerable noise information and gradually lose discrimination, which causes tracking failure.Aiming to address the above problems, this study proposes a long-term target-tracking algorithm based on a perceptual model. The algorithm introduces the background perceptual strategy to solve the problem of traditional correlation filtering lacking real negative samples and the occlusion-sensing strategy to effectively track the occluded target. The proposed algorithm first increases the number of training samples by enlarging the sampling area. A cropping matrix is then introduced into the algorithm to crop shifted samples and obtain complete and valid samples.This method overcomes the boundary effect problem caused by cyclically shifted samples. A classification pool is subsequently constructed by using the corresponding classifiers of a certain number of frames in the case of no occlusion. In the case of severe occlusion, the optimal classifier is finally selected from the classification pool by minimizing the energy function for redetection to achieve long-term target tracking.Result The performance of the proposed algorithm is evaluated by using a public data set. The proposed algorithm has a success rate of 0.990 and an accuracy of 0.988. These values are respectively 2.7% and 2.5% higher than those of the background-aware correlation filter algorithm. The overall success rate and accuracy of the proposed algorithm are considerably higher than those of other algorithms because of the introduction of background and occlusion perception strategies. The tracking accuracy for a single sequence is also higher. However, other algorithms have certain advantages in specific scenarios, and the proposed algorithm does not rank first in the accuracy and success rate of each sequence. The time complexity of the algorithm is slightly higher and the real-time performance is insufficient because of the introduction of perception module. Conclusion Experiments show that the proposed algorithm can accurately track a target under complex conditions, such as severe occlusion, scale change, and target deformation and has certain research value.
Keywords
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