改进特征与GPU加速的行人检测

目的 目前行人检测存在特征维度高、检测耗时的问题,行人图像易受到光照、背景、遮挡等影响,给实际行人检测造成了一定困难。为了提高检测准确性,减少检测耗时,针对以上问题,提出一种改进特征与GPU （graphic processing unit）加速的行人检测算法。方法 首先,采用多尺度无缩放思想,通过canny算子对所有样本进行预处理,减少背景干扰与统一归格化的形变影响。然后,针对实际视频中的遮挡问题,把图像分成头部、左臂、上身、右臂、左腿、右腿6个区域。接着选取比LBP （local binary patterns）特征鲁棒性更好的SILTP （scale invariant local ternary pattern）特征作为纹理特征,在GPU空间中并行提取;同时,分别提取6个区域的HOG （histogram of oriented gradient）特征值,结合行人轮廓在6个区域上的梯度方向分布特性,对其进行加权。最后,将提取的全部特征输出到CPU （central processing unit）,利用支持向量机（SVM）分类器实现行人检测。结果 在INRIA、NICTA数据集上进行实验,INRIA数据集上检测率达到99.80%,NICTA数据集上检测率达到99.91%,并且INRIA数据集上检测时间加速比达到12.19,NICTA数据集上达到13.49,相对传统HOG、LBP算法,检测率、时间比实现提高。结论 提出的改进HOG-SILTP特征与GPU加速的行人检测算法,能够有效表达行人信息,改善传统特征提取方式带来的耗时与形变影响,对环境变化、遮挡具有较强的鲁棒性。该算法在检测率、检测时间方面均有提高,能够实现有效、快速的行人检测,具有实际意义。     

基于多级特征的红外图像行人检测算法

由于可见光图像和红外图像的成像原理不同,可见光图像的行人检测算法难以直接应用于红外图像中.为此,提出一种基于多级梯度特征的红外图像行人检测算法.使用改进的图像显著性检测算法提取红外图像的关键区域,应用质心重定位的滑窗算法快速定位其中的高亮区,采用Zernike矩判断图像的对称性及与行人特征的相似性,通过基于边缘信息输入的卷积神经网络模型逐级缩小判定范围.在OTCBVS红外图像行人数据集上的实验结果表明,与稀疏表示算法相比,该算法的检测准确率较高.     

基于轨迹行为模式特征的视频拷贝检测算法

为了有效地利用视频的时域运动信息来提高视频拷贝检测的精度和鲁棒性,提出一种基于特征点轨迹行为模式的拷贝检测算法.首先从视频连续帧中提取特征点轨迹的行为模式特征,然后采用视觉关键词典技术构造视频的运动特征,最后基于运动特征的相似度进行视频拷贝检测.该算法在TRECVID标准数据集上取得了较高的检测精度.实验分析表明,基于轨迹的运动特征具有较强的描述区分能力,对各种常见的拷贝变化具有鲁棒性.     

Multistage particle windows for fast and accurate object detection

The common paradigm employed for object detection is the sliding window (SW) search. This approach generates grid-distributed patches, at all possible positions and sizes, which are evaluated by a binary classifier: The tradeoff between computational burden and detection accuracy is the real critical point of sliding windows; several methods have been proposed to speed up the search such as adding complementary features. We propose a paradigm that differs from any previous approach since it casts object detection into a statistical-based search using a Monte Carlo sampling for estimating the likelihood density function with Gaussian kernels. The estimation relies on a multistage strategy where the proposal distribution is progressively refined by taking into account the feedback of the classifiers. The method can be easily plugged into a Bayesian-recursive framework to exploit the temporal coherency of the target objects in videos. Several tests on pedestrian and face detection, both on images and videos, with different types of classifiers (cascade of boosted classifiers, soft cascades, and SVM) and features (covariance matrices, Haar-like features, integral channel features, and histogram of oriented gradients) demonstrate that the proposed method provides higher detection rates and accuracy as well as a lower computational burden w.r.t. sliding window detection.     

基于YOLO的多模态加权融合行人检测算法

在夜间光照不足、目标被遮挡导致信息缺失以及行人目标多尺度的情况下,可见光单模态行人检测算法的检测效果较差。为了提高行人检测器的鲁棒性,基于YOLO提出一种可见光与红外光融合的行人检测算法。使用Darknet53作为特征提取网络,分别提取2个模态的多尺度特征。对传统多模态行人检测算法所使用的concat融合方式进行改进,设计结合注意力机制的模态加权融合层,以加强对融合特征图的模态选择。在此基础上,使用多尺度的融合特征进行行人检测。实验结果表明,模态加权融合较concat融合有较大的精度提升,且该算法在夜间光照不足、目标遮挡和目标多尺度情况下检测效果良好,在KAIST数据集上的检测精度优于HalFusion和Fusion RPN+BDT等算法,检测速度也有较大提升。     

特征金字塔融合的多模态行人检测算法

针对单模态行人检测在光照条件较差、目标部分遮挡、目标多尺度时检测效果较差的问题，提出了一种基于可见和红外双模态特征金字塔融合的行人检测算法。使用深度卷积神经网络代替传统的手工设计特征方式分别自动从可见模态及红外热模态的图片中提取单模态特征，根据ResNet（Residual Net）的阶段性特征图谱搭建特征金字塔网络，生成每个模态的特征金字塔，并将两个模态的特征金字塔进行逐层融合。选择深度学习通用目标检测算法--Faster R-CNN作为后续的目标定位与分类算法来解决多模态行人检测问题。在特征金字塔融合阶段，针对级联融合和较大值融合容易忽略弱特征，无法有效融合互补特征的问题，提出了一种锐化特征的特征金字塔融合方法，根据阈值强化突出强特征，互补叠加弱特征，有效利用每个模态的特征，进一步提高模型的检测效果。实验结果表明，特征金字塔聚合的多模态行人检测算法可以有效解决多模态行人检测问题，在KAIST数据集上的检测效果超过了目前该数据集上的最佳模型。     

改进型HLBP纹理特征的行人检测

在行人检测中,Haar型LBP(HLBP)特征采用局部统计方式,有效地降低了噪声影响,相比LBP特征对图像纹理描述有明显优势。但是,HLBP特征在计算特征值时,中心点没有参与计算,导致其信息没有被利用。针对这一不足,提出了改进型HLBP(IHLBP)特征,该方法令中心点参与到计算工作中,并赋予其最大权值。首先利用二维离散Haar小波变换,对图像做两级分解处理,得到三种不同尺度图像;然后针对上述三种图像分别提取IHLBP特征并做归一化处理,最后串接三组特征得到最终的特征向量。在INRIA Person数据集上,采用SVM进行测试。实验结果表明,该方法能有效地提高行人检测识别率。     

Dual adaptive alignment and partitioning network for visible and infrared cross-modality person re-identification

Visible and infrared person re-identification (VI-ReID) describes the task of matching the images of a person, captured by visible-light and infrared cameras; this is a particular challenge in night time surveillance applications. Existing cross-modality recognition studies have been conducted mainly with a focus on learning the global and shareable feature representation of pedestrians to handle cross-modality discrepancies. However, the global features of pedestrian images cannot solve the unaligned image pairs efficiently, particularly when encountering the human appearance or posture misalignment caused by inaccurate pedestrian detection boxes. To mitigate the impact of these problems, we propose an end-to-end dual alignment and partitioning network to simultaneously learn global and local modal invariant features of pedestrians. First, we use two adaptive spatial transform modules to align the visible and infrared input images. Subsequently, the aligned image is divided horizontally, and the features of each local block are extracted. Then, we fuse these local features with global features. To alleviate the differences between heterogeneous modals and learn the common feature representation of heterogeneous modals, we map the features of heterogeneous modes into the same feature embedding space. Finally, we use the combination of identity loss and weighted regularized TriHard loss to improve the recognition accuracy. Extensive experimental results on two cross-modality datasets, RegDB and SYSU-MM01, demonstrate the superiority of the proposed method over other existing state-of-the-art methods.

     

基于分块特征收缩的行人检测方法

针对基于梯度方向直方图(Histogram of Oriented Gradient,HOG)特征和局部二值模式(Local Binary Patterns,LBP)特征的行人检测存在特征向量维度大、检测精度有待提高的问题,提出了一种分块特征收缩的行人检测方法。首先将样本图像划分成多个大小相同的重叠分块;然后提取各分块的HOG和LBP特征,并将两种特征融合作为分块的特征,通过该特征来训练分块分类器,根据分块分类器的行人检测精度对分块进行排序,选取检测精度较高的分块进行特征收缩;最后将特征收缩后的分块特征向量连接在一起作为最终用于行人检测的特征。在INRIA公共测试集合上的实验结果表明,该方法在降低了特征向量维度的同时提高了行人检测精度。     

Fusion of visible and thermal images for facial expression recognition

Most present research into facial expression recognition focuses on the visible spectrum, which is sensitive to illumination change. In this paper, we focus on integrating thermal infrared data with visible spectrum images for spontaneous facial expression recognition. First, the active appearance model AAM parameters and three defined head motion features are extracted from visible spectrum images, and several thermal statistical features are extracted from infrared (IR) images. Second, feature selection is performed using the F-test statistic. Third, Bayesian networks BNs and support vector machines SVMs are proposed for both decision-level and feature-level fusion. Experiments on the natural visible and infrared facial expression (NVIE) spontaneous database show the effectiveness of the proposed methods, and demonstrate thermal IR images’ supplementary role for visible facial expression recognition.     

域通用和域分离字典对学习的行人重识别算法

为克服不同相机视角之间的域偏移问题,提出一种基于域通用和域分离字典对学习的跨视角行人重识别算法。具体地,基于来自同一相机视角下的行人共享相同的域,并且同一视角中每个行人图像所携带的域信息在短时间内具有一致性,将同一视角下的行人图像分解为特定视角的域信息分量和域分离的行人外观特征分量,提出一个判别字典学习模型以创建用于描述域信息分量的域通用字典和描述行人外观分量的域分离字典。由于来自同一相机视角下的图像具有域相似性,因此通过低秩正则化来细化用于表示域信息的字典。为了进一步提高学习字典的判别能力,在算法中约束相同视角、相同身份的多幅图像的编码系数具有很强的相似性。此外,采用一种新颖的扩展正则化方法来解决不同行人相似外貌特征和同一行人不同外貌特征的视觉外观歧义问题。在四个具有挑战性的数据集上进行实验,结果表明域通用和域分离字典对学习的算法相对于一些现有最新算法更具有效性和优越性。     

Infrared ship target segmentation through integration of multiple feature maps

We investigate the issue of ship target segmentation in infrared (IR) images, and propose an efficient method based on feature map integration. It consists of mainly two procedures: salient region detection based on multiple feature map integration and salient region segmentation based on locally adaptive thresholding. Firstly, a saliency map is constructed by integrating multiple features of IR ship targets, including gray level intensity, local contrast, salient linear structures, and edge strength. Secondly, we propose an adaptive thresholding method to segment each local salient region, and a target selection procedure based on shape features is used to remove background and obtain the true target. Experimental results show that the proposed method performs well for IR ship target segmentation. The advantage of the proposed method is demonstrated in both visual and quantitative comparisons, especially for IR images with a bright background or a ship target close to port.     

Multiperson interaction recognition in images: A body keypoint based feature image analysis

Most interaction recognition approaches have been limited to single‐person action classification in videos. However, for still images where motion information is not available, the task becomes more complex. Aiming to this point, we propose an approach for multiperson human interaction recognition in images with keypoint‐based feature image analysis. Proposed method is a three‐stage framework. In the first stage, we propose feature‐based neural network (FCNN) for action recognition trained with feature images. Feature images are body features, that is, effective distances between a set of body part pairs and angular relation between body part triplets, rearranged in 2D gray‐scale image to learn effective representation of complex actions. In the later stage, we propose a voting‐based method for direction encoding to anticipate probable motion in steady images. Finally, our multiperson interaction recognition algorithm identifies which human pairs are interacting with each other using an interaction parameter. We evaluate our approach on two real‐world data sets, that is, UT‐interaction and SBU kinect interaction. The empirical experiments show that results are better than the state‐of‐the‐art methods with recognition accuracy of 95.83% on UT‐I set 1, 92.5% on UT‐I set 2, and 94.28% on SBU clean data set.     

基于二值空间线特征的道路检测方法

基于视觉的道路检测是高级驾驶员辅助系统（Advanced Driver Assistance Systems,ADAS）的核心技术。针对空间线模型（SPatial RAY feature）对道路宽度适应能力弱,时间复杂度高的不足,提出了一种改进的空间线模型,利用基础分类器得到的置信度图提取二值SPRAY特征,引入帧间信息复用机制来提高道路区域检测的效率。大量结构化和半结构化道路图像的检测实验证明了该方法能够有效提高道路检测的精度,同时能提升空间线模型对不同宽度道路检测的鲁棒性。     

改进的红外图像行人检测和交叠率算法

行人越界入侵报警是十分普遍的应用场景,尤其是在安保领域.本文设计了一种改进的红外图像行人检测和交叠率算法,两者结合可以实现对行人的越界报警.本方法主要由三部分组成：红外图像行人检测算法、目标分类算法、交叠率算法与报警逻辑.红外图像是为了尽量克服环境影响,并且在夜间也具有良好的显示与图像采集功能;行人检测是通过YOLOv3算法和基于方向梯度直方图（HOG）特征的多层感知器（MLP）二分类来实现;报警算法与逻辑是计算目标的候选框与报警区域的交叠率,再进行逻辑判断.实验表明,本方法准确性高,报警准确率可达91%,有良好的应用价值.     

Facial expression recognition based on dual-feature fusion and improved random forest classifier

Facial expression recognition (FER) is an important means for machines to understand the changes in the facial expression of human beings. Expression recognition using single-modal facial images, such as gray scale, may suffer from illumination changes and the lack of detailed expression-related information. In this study, multi-modal facial images, such as facial gray scale, depth, and local binary pattern (LBP), are used to recognize six basic facial expressions, namely, happiness, sadness, anger, disgust, fear, and surprise. Facial depth images are used for robust face detection initially. The deep geometric feature is represented by point displacement and angle variation in facial landmark points with the help of depth information. The local appearance feature, which is obtained by concatenating LBP histograms of expression-prominent patches, is utilized to recognize those expression changes that are difficult to capture by only the geometric changes. Thereafter, an improved random forest classifier based on feature selection is used to recognize different facial expressions. Results of comparative evaluations in benchmarking datasets show that the proposed method outperforms several state-of-the-art FER approaches that are based on hand-crafted features. The capability of the proposed method is comparable to that of the popular convolutional neural-network-based FER approach but with fewer demands for training data and a high-performance hardware platform.     

基于局部二值模式的中值滤波检测算法

近年来,数字图像盲取证技术是国际上新兴的一个研究领域,针对数字图像处理及篡改中一种常用的处理模式---中值滤波,提出一种基于局部二值模式（LBP）的中值滤波篡改检测方法。首先对待测图像利用基于中心对称像素的局部二值模式算子（C-LBP）定位其特征提取区域,随后基于局部区域中像素之间的关系提出一种改进型的局部二值模式算子,并将其直方图统计作为最终的检测特征。实验证明,与其他传统滤波检测算法相比,该算子不仅大大降低了计算时间,而且在低维度的条件下取得了较高的检测率,是一种有效的中值滤波检测算子。     

基于图切割和密度聚类的视频行人检测算法*

现有视频行人检测方法把行人检测看成一个有监督的两类(即行人和背景)学习问题,区分视频中的行人和背景,并不能很好解决行人的姿态变化和行人间的遮挡问题.文中提出基于图切割和密度聚类的行人检测算法,把行人检测看成一个多类的无监督学习过程.在训练阶段,首先对每个训练样本计算多级梯度方向直方图-局部二分模式(HOG-LBP)特征,然后对多级HOG-LBP特征所属的每个图像块分配不同的权值.为了区别行人的不同部位并赋权值,采用基于图像块的图分割方法从背景中分割行人所在的图像块.最后,再采用基于密度峰值的聚类算法对正样本和负样本分别进行无监督的聚类.在测试阶段,首先通过计算样本特征与每个聚类中心的距离,然后使用前5个最短距离进行投票,判断其是否包含行人.实验证明,文中算法较好解决行人的姿态变化和行人间的遮挡问题,并且随着训练样本的增加,能取得和目前最优行人检测方法可比较的结果.     

红外图像序列中的人体检测算法

为克服亮度分布惯性特征不能充分体现人体区域亮度特征的不足,提出一种红外图像序列中的人体检测算法。采用MAP MRF模型得到人体可能存在的感兴趣区域(ROI),根据红外图像中人体的成像特点,在以ROI中心点为圆心的各个圆环域中统计亮度信息,构建基于亮度 距离联合空间的人体特征,并采用支持向量机分类器对候选区域进行分类检测。在不同红外图像序列中的实验结果均表明,该算法具有较好的鲁棒性。