共查询到18条相似文献,搜索用时 46 毫秒
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所做的工作是利用粒子滤波理论解决目标跟踪所面临的技术问题。首先介绍粒子滤波中的两种重要算法:贝叶斯理论和蒙特卡罗方法,接着在此基础上详细阐述基于粒子滤波的目标跟踪算法。 相似文献
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一类基于信息融合的粒子滤波跟踪算法 总被引:2,自引:3,他引:2
本文提出了一种基于图像多特征信息融合的粒子滤波跟踪算法.该算法利用颜色柱状图描述运动目标颜色分布信息,帧间差的梯度图像描述目标运动信息,并在柱状图框架下给出了运动目标颜色和运动似然模型,保证了颜色和运动似然模型在尺度上的统一.由于图像多特征提供了运动目标多方面的测量信息,从而提高了算法的可靠性.试验表明该算法在使用相同粒子数目的情况下较采用单一颜色特征的粒子滤波跟踪算法效果好. 相似文献
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提出了一种将粒子滤波和CamShift相结合的多特征视觉跟踪方法.通过CamShift对粒子的位置和尺度同时进行优化,使得跟踪窗口能随着目标尺度的大小变化相应调整.同时采用自适应方式将颜色信息和运动信息在CamShift优化的粒子滤波框架下有效结合起来.该方法使用CamShift对粒子传播进行优化,每个粒子都收敛到目标附近,粒子的有效性得到提高.实验结果表明,使用10个粒子的CamShiit优化的粒子滤波的跟踪误差小于100个粒子的传统粒子滤波的跟踪误差.并且由于多特征的使用,目标在受到背景相似物体干扰和场景光线发生显著变化等情况下仍能实现稳定的跟踪.用较少的粒子就能实现稳定的跟踪,减少了计算代价,提高了跟踪的鲁棒性. 相似文献
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基于遗传进化策略的粒子滤波视频目标跟踪 总被引:1,自引:0,他引:1
粒子退化问题是影响基于粒子滤波视觉跟踪性能的一个重要因素,为克服这种缺陷,本文将遗传进化策略引入粒子滤波跟踪算法,利用遗传算法的选择策略,根据预定的似然阈值迭代选择每代粒子中次优个体,然后对未选中的粒子实施交叉、变异操作以获得粒子的多样性,从而有效解决了粒子的退化问题.另外,针对跟踪中目标表观变化的问题,本文提出的跟踪算法采用了多模板自适应更新技术以确保跟踪的准确性.实验结果表明,本文提出的跟踪算法能有效地跟踪室内运动目标,并对光照变化、目标姿态变化具有良好的鲁棒性. 相似文献
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针对环境迁移、目标被遮挡或姿态变化较大时传统粒子滤波算法的鲁棒性不强的问题,提出一种改进的粒子滤波目标跟踪算法。建立目标模型时,将目标的HSV颜色特征和Uniform LBP纹理特征进行加权融合;粒子重采样过程中,采用加权随机采样方法,将粒子权值作为重采样的影响因子而非决定因子,以提升粒子多样性,降低粒子衰退对目标跟踪的影响;目标被干扰时,采用卡尔曼滤波对目标位置进行偏移校正,以获取目标正确位置;最后采用模板更新策略对目标模板进行实时更新。实验结果表明:相较于传统粒子滤波算法和CMT算法,本文算法对复杂环境中目标被遮挡和姿态变化的情况下都具有较好的鲁棒性。 相似文献
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Traffic sign recognition (TSR), as a critical task to automated driving and driver assistance systems, is challenging due to the color fading, motion blur, and occlusion. Traditional methods based on convolutional neural network (CNN) only use an end-layer feature as the input to TSR that requires massive data for network training. The computation-intensive network training process results in an inaccurate or delayed classification. Thereby, the current state-of-the-art methods have limited applications. This paper proposes a new TSR method integrating multi-layer feature and kernel extreme learning machine (ELM) classifier. The proposed method applies CNN to extract the multi-layer features of traffic signs, which can present sufficient details and semantically abstract information of multi-layer feature maps. The extraction of multi-scale features of traffic signs is effective against object scale variation by applying a new multi-scale pooling operation. Further, the extracted features are combined into a multi-scale multi-attribute vector, which can enhance the feature presentation ability for TSR. To efficiently handle nonlinear sampling problems in TSR, the kernel ELM classifier is adopted for efficient TSR. The kernel ELM has a more powerful function approximation capability, which can achieve an optimal and generalized solution for multiclass TSR. Experimental results demonstrate that the proposed method can improve the recognition accuracy, efficiency, and adaptivity to complex travel environments in TSR. 相似文献
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一种新型自动激光经纬仪引导跟踪方法 总被引:2,自引:2,他引:0
针对传统电子经纬仪需要人眼瞄准读数,测量效率低下且测量精度易受瞄准误差影响的问题,本文提出一种新型的自动激光经纬仪系统,无需人眼瞄准即可实现经纬仪系统自动瞄准测量.该方法采用精密二维转台和高分辨率相机代替人眼,利用TM5100A的马达驱动功能将经纬仪的望远镜指向相机视场范围,实现引导跟踪功能.然后,再进一步识别并瞄准视场内的目标,完成对目标的识别和测量过程.本文主要就该系统中的跟踪引导算法进行了研究和分析.仿真和实验结果表明,该引导方法效率高,实用性强,能够较好地实现相机对经纬仪激光点的准确引导,保证了经纬仪测量系统的引导精度. 相似文献
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Road traffic sign recognition is an important task in intelligent transportation system. Convolutional neural networks (CNNs) have achieved a breakthrough in computer vision tasks and made great success in traffic sign classification. In this paper, it presents a road traffic sign recognition algorithm based on a convolutional neural network. In natural scenes, traffic signs are disturbed by factors such as illumination, occlusion, missing and deformation, and the accuracy of recognition decreases, this paper proposes a model called Improved VGG (IVGG) inspired by VGG model. The IVGG model includes 9 layers, compared with the original VGG model, it is added max-pooling operation and dropout operation after multiple convolutional layers, to catch the main features and save the training time. The paper proposes the method which adds dropout and Batch Normalization (BN) operations after each fully-connected layer, to further accelerate the model convergence, and then it can get better classification effect. It uses the German Traffic Sign Recognition Benchmark (GTSRB) dataset in the experiment. The IVGG model enhances the recognition rate of traffic signs and robustness by using the data augmentation and transfer learning, and the spent time is also reduced greatly. 相似文献
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中国城市道路指路标志典型版面布局导航绩效研究 总被引:1,自引:0,他引:1
目的对5款典型的省会城市指路标志导航绩效进行工效学评价,并确定其存在的问题。方法 34名大学生被试参与完成5款指路标志版面布局的道路关系判断与道路走向判断两类实验任务。结果 5款指路标志任务绩效无显著差异,任务正确率介于32%~38%,具体任务分析发现问题主要集中于版面两侧道路关系判断及道路走向判断,其平均正确率仅为10%和18%。结论 (1)现有5款典型版面布局导向绩效无显著差异,总体绩效均很低;(2)原因主要是路名(尤其是版面两侧路名)的布局方式对被试的道路走向判断及道路关系判读具有强烈的暗示诱导作用。 相似文献