搜索资源列表
cr1565
- STEREO-BASED ELLIPTICAL HEAD TRACKING-A novel algorithm for head-tracking based on stereo is presented in this paper. Employing stereo vision makes the tracker robust to many factors such as clutter, color and lighting variations, that affect c
Visualtrackerautomatically
- 本文研究的是设计一个控制系统，用固定在云台上的摄像头来跟踪运动目标。具体包括硬件设计和软件编程。利用计算机、摄像机、图像采集卡、步进电机驱动器和步进电机等硬件设备设计一套视觉自动跟踪系统，利用VC++实现上位机串口通信程序；利用KeilC51实现下位机的控制功能-This paper is to design a control system, using a fixed camera in the head Yuntai tracking moving targets. Including h
BLOCK-PHASE-DEMODULATOR
- 块相位估计－Block Phase Estimation(BPE)取代了传统的锁相环(PLL)结构，以取模 (Modulo)运算为基础，而且使用创新的平均器，既简化了算法结构，又大大方便于硬件实现，同时为芯片设计提出了新的思路；但国内研究较少，资料极其匮乏；文章在对BPE的原理与结构进行较为深入的介绍与分析的基础上，对BPE进行了由浅入深的仿真，解决了部分跟踪器－SECTOR TRACKER(ST)的问题，给出了部分仿真结果；最后，分析了BPE的优缺点，给出了改进的方法。-Block Phase
382
- Tracking people in a dense crowd is a challenging problem for a single camera tracker due to occlusions and extensive motion that make human segmentation difficult. In this paper we suggest a method for simultaneously tracking all people in a
MODEL-INCREMENTAL
- 2011 paper in IEEE in maximum power point tracker
BoostingTracker_Source_v03
- 在线boost算法，来自文章On-line Boosting and Vision-boost tracker from paper:: On-line Boosting and Vision
Fast-Tracking
- “Fast Tracking via Dense Spatio-Temporal Context Learning,” In ECCV 2014的源代码，效果非常好。-In this paper, we present a simple yet fast and robust algorithm which exploits the spatio-temporal context for visual tracking. Our approach formulates the spatio-te
B.Lucas
- 这是KLT算法论文的高清版。Kanade-Lucas-Tomasi方法，在跟踪方面表现的也不错，尤其在实时计算速度上，用它来得到的，是很多点的轨迹“trajectory”，并且还有一些发生了漂移的点，所以，得到跟踪点之后要进行一些后期的处理，说到Kanade-Lucas-Tomasi方法，首先要追溯到Kanade-Lucas两人在上世纪80年代发表的paper：An Iterative Image Registration Technique with an Application to Ste
stanford_LK
- Lucas-Kanade 跟踪算法，此算法实现了经典Lk跟踪，详细算法参见文章 An Iterative Image Registration Technique with an Application to Stereo Vision (IJCAI), B.D. Lucas and T. Kanade, 7th International Joint Conference on Artificial Intelligence (IJCAI), April, 1981, pp. 674-67
csr-dcf-master
- 17年cvpr上的目标跟踪方法CSR-DCF的matlab源码(Matlab implementation of the DCF-CSR tracker from the paper published in the proceedings of Conference on Computer Vision and Pattern Recognition (CVPR) 2017.)
Matlab_STCv0
- In this paper, we present a simple yet fast and robust algorithm which exploits the spatio-temporal context for visual tracking. Our approach formulates the spatio-temporal relationships between the object of interest and its local context based on a