Research on Target Tracking Algorithm from Fisheye Camera Based on Compressive Sensing
Funds:
The Natural Science Foundation of Hebei Province (F2015203212)
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摘要: 該文針對畸變嚴重的魚眼圖像中的目標跟蹤,提出一種能適應尺度變化、姿態(tài)變化以及形狀畸變的魚眼視頻目標跟蹤的方法。該方法首先將灰度特征和相對梯度特征相結合得到目標的高維特征,然后對其平均降維得到目標的壓縮特征。并根據(jù)魚眼成像模型得到投影點的運動特性,確定目標的運動范圍。為了適應尺度變化,在塊匹配運動估計思想的基礎上,對目標跟蹤框的頂點分別進行由粗到精的定位,并在此過程中根據(jù)跟蹤框的尺度相應改變壓縮特征的尺度。實驗結果表明:該算法在目標畸變、尺度變化、姿態(tài)變化以及局部遮擋等情況下,判斷指標均優(yōu)于其他對比算法。Abstract: For object detection in fisheye images which present serious distortion, an object tracking method is proposed to deal with scale variance, pose change and distortion. Firstly, gray feature and gradient feature are combined to obtain a high dimensional feature of the target, then reduce its dimensionality by averaging to obtain targets compressive feature. According to fisheye imaging model, motion of object point is modeled, and range of motion of target is predicted. In order to adjust to scale variance, corner points are positioned respectively in a coarse to fine manner based on the block matching motion estimation, and the scale of compressed feature is changed along with scale change of object box. Experimental results show that the proposed algorithm is superior to other algorithms in the case of distortion, scale change, pose change and part occlusion.
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