weixin_42438346的博客

数据读取：Dataloadersampler --> indexDataset --> image, labelepoch，iteration，batchsize样本总数：80 batchsize：81个epoch = 10个iteration先Dataset，再Dataloader数据预处理：transforms

mot 评价指标

正常卷积（convolution）torch.nn.Conv2d(in_channels, out_channels, kernel_size, stride = 1, padding = 0, dilation = 1, groups = 1, bias = True)反卷积torch.nn.ConvTranspose2d(in_channels, out_channels, kernel_size, stride = 1, padding = 0, output_padding = 0, gro

selectROI(windowName, img, showCrosshair=None, fromCenter=None): . 参数windowName：选择的区域被显示在的窗口的名字 . 参数img：要在什么图片上选择ROI . 参数showCrosshair：是否在矩形框里画十字线. . 参数fromCenter：是否是从矩形框的中心开始画返回值为一个元祖(min_x, min_y, w, h)第一个值为矩形框中最小的x值第二个值为矩形框中

Types of object detectorsone-stage detectorstwo-stage detectorscornet：

解释，阐明（explain）：elucidate为了：so as to决定：determine利用：utilize反过来也一样：vice versaWith these criteria, zero MTMCT loss would imply zero Re-ID loss, but not vice versa.

abstract：MTMCT: 从多个摄像头采集的视频中跟踪多个人。(MTMCT) tracks many people through video taken from several cameras.Re-id：从一系列图片中检索与一张被查询图片相似的图片。(Re-ID) retrieves from a gallery images of people similar to a person query image.我们用CNN为MTMCT和Reid学习好的特征We learn goo

多目标跟踪的目的就是将每个目标和其他目标进行区分开来，具体方法是给每个目标分配一个ID，并记录他们的轨迹。和目标检测的最大区别：仅仅使用目标检测无法给对象分配ID，并且多目标跟踪算法能够优化轨迹，能让目标框更加稳定。多目标跟踪中一个比较经典的和深度学习结合比较好的方法就是Detection Based Tracking，对前后两帧进行目标检测，然后根据得到的前后两帧的所有目标进行匹配，从而维持ID。初学者接触比较多的就是SORT和Deep SORT算法了。MOT中的评价指标评价出发点：..

参考：https://mp.weixin.qq.com/s/FO8-uCkg6D6yDf7Cv01DAQ网络结构：centernetcentertrackCenterTrack localizes objects and predicts their associations with the previous frame即多出来一个分支用来做时间维度的associationThe architecture of CenterTrack is essentially identical t

FairMOT [1] is a one-shot multi-object tracker (MOT) that combines and performs both the Object Detection and Re-ID tasks collectively.DLA（Deep Layer Aggregation）