Domain adaptation is an active, emerging research area that attempts to address the changes in data distribution across training and testing datasets. With the availability of a multitude of image acquisition sen- sors, variations due to illumination, and viewpoint among others, com- puter vision applications present a very natural test bed for evaluating domain adaptation methods. In this monograph, we provide a compre- hensive overview of domain adaptation solutions for visual recognition problems. By starting with the problem description and illustrations, we discuss three adaptation scenarios namely, (i) unsupervised adap- tation where the “source domain” training data is partially labeled and the “target domain” test data is unlabeled, (ii) semi-supervised adaptation where the target domain also has partial labels, and (iii) multi-domain heterogeneous adaptation which studies the previous two settings with the source and/or target having more than one domain, and accounts for cases where the features used to represent the data in each domain are different. For all these topics we discuss existing adaptation techniques in the literature, which are motivated by the principles of max-margin discriminative learning, manifold learning, sparse coding, as well as low-rank representations. These techniques have shown improved performance on a variety of applications such as object recognition, face recognition, activity analysis, concept clas- sification, and person detection. We then conclude by analyzing the challenges posed by the realm of “big visual data”, in terms of the generalization ability of adaptation algorithms to unconstrained data acquisition as well as issues related to their computational tractability, and draw parallels with the efforts from vision community on image transformation models, and invariant descriptors so as to facilitate im- proved understanding of vision problems under uncertainty.

主要用于基于深度学习的目标识别网络训练数据集，共300张左右图片

针对图像的目标识别问题,采用视觉感知的方法,模拟感受野的分层信息处理机制,并引入神经元间的侧抑制机制,对神经元响应进行筛选,通过检测视觉基本特征的方式识别图像中的目标.算法首先在简单细胞的感受野中对图像进行预处理;其次,在复杂细胞的感受野中,将简单细胞的感受野刺激进一步拓扑特征提取,得到感受野刺激响应;最后,通过侧抑制机制对响应神经元筛选,找出对刺激响应较强烈的神经元,将其输出作为目标识别的参数标准.实验结果表明,基于视觉感知的算法可以用少量样本解决大量图像中的目标识别问题,识别率高于边缘检测和图像分割方法,算法的目标识别率达到95.56%.

这是2020年IEEE上的论文，自己要用没找到翻译版本就翻译了一下，翻译不好。只是分享出来大家共同参考，有需要的请自取

基于特征匹配的舰载对陆导弹目标识别模型

基于OpenCV的目标跟踪，能够识别人体简单的动作，附带测试视频。

opencv 目标识别，行人跟踪测试视频，5分钟长，含单人，多人，物体遮挡等多种街头场景，基本满足测试所需。

MSTAR数据集，用于检验SAR图像目标识别算法的效果，内附转换JPG，TIFF代码

14篇论文内容分别如下： R-CNN，Fast R-CNN，Faster R-CNN，Cascade R-CNN，Mask R-CNN，Grid R-CNN，R-FCN，YOLO，SSD，FPN，RetinaNet，CornerNet，FoveaBox，Ours

本文设计了一种基于支持向量机（SVM）的运动目标识别算法，以对运动目标进行准确的识别和分类。 鉴于支持向量机在小样本，非线性和高维模式识别方面的优势，构造了一种基于支持向量机的分类器。 利用形状特征构成的特征向量分类样本对支持向量机进行训练和分类，结合支持向量机和二叉决策树形成多分类器。 对象特征向量用作SVM的输入，我们将使用分类器对检测到的运动对象进行分类。 实验结果表明，该算法能够准确识别和分类视频图像中的不同对象。