CS中"刷屏"这种行为用英语怎么说请问各位大大,CS中一个人连续杀了很多人.以至于他的FRAG把屏幕上方占满了,这样的行为用英语怎么说?希望能地道点、口语点的,不要翻译器另外希望不要回答h11、sv_restartround之类的100分不成敬意我知道刷屏的意思.而我问题中所提到的只是一种戏称,杀人杀得太多以致于像在说话刷屏一样.希望能有达人为我解答
刷屏和你描述的不对.刷屏 在CS 中 是指 死亡后 在 控制台里 用say 命令在短时间内发大量的话 .楼主说的连续杀了很多人,以至把屏幕上方占满,这样的行为 只能 是 连杀了 .-------------------------------以下摘自网络双连杀 DOUBLE KILL 三连杀 MULTI KILL 四连杀 OUAGE KILL 五连杀 UNBREAKBLE 六连杀 UNBELIEVABLE 七连杀 YOU WANNA A PIECE OF ME?八连杀 COME GET SOME ----------------------------------------------- 以上 若有所错误,请网友指正.
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不良信息举报***:>>,,,>【CS】1.6最经典的Frag镜头集锦
【CS】1.6最经典的Frag镜头集锦
15:21:51条 来源：多玩 作者：多玩CS玩家
1999年的今天，史上最伟大的FPS游戏之一----Counter-Strike正式发售。有幸见证一个伟大游戏的诞生，一个让一代玩家发现新世界般欣喜若狂、留下无数美好回忆的传奇。每个人都有难舍的回忆，所有人的回忆交集在一个dust2门洞里时，这就是游戏的艺术，这就是时代的光芒。
要不要挑一下？Fragtrack - Robust Fragments-based Tracking
Fragtrack - Robust Fragments-based Tracking using
the Integral Histogram
In this work we apply a recognition-by-parts approach to object tracking.
The template object is represented by multiple image fragments or patches.
The patches are arbitrary and are not based on an object model (in
contrast with traditional use of model-based parts e.g. limbs and torso in
human tracking). Every patch votes on the possible positions and scales of the
object in the current frame, by comparing its histogram with the corresponding
image patch histogram. We then minimize a robust statistic in order to combine
the vote maps of the multiple patches.
Fragtrack overcomes several difficulties which cannot
be handled by traditional histogram-based algorithms (e.g. mean shift). First,
by robustly combining multiple patch votes, we are able to handle partial
occlusions or pose change. Second, the geometric relations between the template
patches allow us to take into account the spatial distribution of the pixel
intensities - information which is lost in traditional histogram-based
algorithms. Third, tracking large targets has the same computational cost as
tracking small targets.
Amit Adam, Ehud Rivlin and Ilan Shimshoni, Robust Fragments-based Tracking using the Integral
Histogram (pdf). IEEE Conference on
Computer Vision and Pattern Recognition (CVPR), June 2006
Example Videos
The following videos show the robust tracking achieved by our algorithm. In
all of these examples we have used only the gray-scale information, quantized
to 16 bins. The same parameters were used in all the clips.
Note that these results are obtained using the tracker's raw output. No
filtering whatsoever is employed. Of course the tracker's estimate may be used
as the measurement or update step in a filtering framework.
& face (2.5M)&&&&&&&&&&&
& woman (2.5M)&&&&&&&&
& hug (&1M)
Note: the (standard +-10%) scale solution we implemented has some limitations -
see paper for comments
Below are the sources of a C++ implementation of the tracker, with
instructions for building and running it:
牋 fragtrack_console_application.zip
Original sequences and ground
&&& woman sequence (11M)&&& ground truth
&&&&&&&&&&&&&&