tracking window 中文意思是什麼
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In feature extractor, the paper combines centriod of the moving objects to tracking window to distinguish pedestrian and moving vehicle. similar functions are applied for matching feature
在特徵提取方面，採用了質心和窗口相結合的方法，兼顧了目標的位置和形狀；而且還初步的去除了行人對運動目標跟蹤的干擾。 -
Based on the method, a tracking window method is adopted to track the target, and locate the position of target centroid by adjusting the window self - adaption
本演算法採用動態窗口法，利用目標顏色的概率分佈特徵，鎖定目標的顏色分佈區域的質心，達到快速準確的跟蹤效果。 -
At last the visual c + + realization of the auto tracking controlling system in window - xp environment is discussed. also the hand aidded tracking system ’ s realization in window - xp environment is introduced in this dissertation. the hand aidded tracking data ’ s processing is detailed discussed
並在windows - xp環境下，利用visualc + +環境進行了編程，實現了從誤差數據採集處理及跟蹤信號的處理到伺服控制的自動跟蹤數據傳輸功能。 -
Some brief introduction of the tracking mode, the blocking mode and future airspace window are presented
介紹了跟蹤、攔阻和未來空域窗三種射擊模式的工作原理。 -
Image pre - process is performed by use of median filter. the model of feature tracking window is built up, then, klt feature point detection and tracking algorithm is given out and feature point necessary conditions are discussed
在利用中值濾波器對圖像序列進行預處理后，給出基於特徵的klt特徵點提取與跟蹤演算法，並且分析了特徵點必須滿足的條件。 -
Later on, after elaborating the disadvantages of the old methods in detecting and recognizing moving objects, a series of corresponding approaches are proposed, such as grid scan, local tracking bug and dynamic window in object tracing to reduce the huge data needed to be processed, maximum and minimum for selecting a proper segmentation threshold and improved conversion from rgb model to hsv and so on to decrease the influence of inhomogeneous lighting and the color noise, a bilinear interpolation in each quadrant to eliminate the bad effect on the recognition precise because of the distortions of the camera. after that, much emphasis is given on application study in pattern recognition with a feed - forward neural network. both the basic bp algorithm and improved bp algorithm in the study process are described in detail, and the later is used to quicken convergence speed and improve validity of the network
然後，分析和闡明了傳統的運動目標檢測方法的不足，並在此基礎上結合研究中的實際實驗環境，提出了一系列解決方法，包括針對降低龐大數據量而提出的網格掃描、局部「跟蟲」追蹤和動態窗口掃描等目標檢測方法，針對實驗環境中光照不均和顏色干擾提出基於人機交互的最大最小值閾值選取方法和引入改進的rgb模型到hsv模型的轉換方法，為消除圖像畸變對識別精度的惡劣影響而採用的通過控制點進行雙線性插值進行畸變校正的方法；緊接著，概述了神經網路的發展歷史和幾種常用神經網路模型的特點，重點研究了前饋型神經網路在模式識別中的應用問題，詳細闡述了基本的bp演算法和學習過程中bp演算法的改進，從而使網路收斂速度更快，解決問題更有效，並在此基礎上，設計了一個基於bp神經網路的運動目標識別系統，給出了實驗結果。
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