计算机工程与应用
計算機工程與應用
계산궤공정여응용
COMPUTER ENGINEERING AND APPLICATIONS
2015年
10期
160-165
,共6页
唐宇%凌志刚%李建成%白璐
唐宇%凌誌剛%李建成%白璐
당우%릉지강%리건성%백로
目标跟踪%压缩感知%特征融合%实时跟踪
目標跟蹤%壓縮感知%特徵融閤%實時跟蹤
목표근종%압축감지%특정융합%실시근종
object tracking%compressive sensing%feature fusion%real-time tracking
针对运动目标外观或背景变化较大时,采用基于压缩感知的跟踪算法由于特征单一易导致漂移、跟踪不稳定甚至丢失目标等问题,提出了改进的基于自适应特征融合的压缩感知跟踪算法。该算法采用两种随机测量矩阵,分别投影V、H空间得到压缩后的纹理和颜色特征,利用在线计算的特征可靠性相对程度来自适应调整特征加权系数,充分利用两类特征的互补性来增强跟踪稳定性。对不同视频的测试结果表明,提出的方法在目标外观、背景环境变化时仍能准确跟踪目标,在目标大小为70像素×100像素时平均帧率为22帧/s,达到实时性。与提取单一特征的原压缩感知算法相比,改进后的方法在目标外观和背景变化时具有更强的鲁棒性。
針對運動目標外觀或揹景變化較大時,採用基于壓縮感知的跟蹤算法由于特徵單一易導緻漂移、跟蹤不穩定甚至丟失目標等問題,提齣瞭改進的基于自適應特徵融閤的壓縮感知跟蹤算法。該算法採用兩種隨機測量矩陣,分彆投影V、H空間得到壓縮後的紋理和顏色特徵,利用在線計算的特徵可靠性相對程度來自適應調整特徵加權繫數,充分利用兩類特徵的互補性來增彊跟蹤穩定性。對不同視頻的測試結果錶明,提齣的方法在目標外觀、揹景環境變化時仍能準確跟蹤目標,在目標大小為70像素×100像素時平均幀率為22幀/s,達到實時性。與提取單一特徵的原壓縮感知算法相比,改進後的方法在目標外觀和揹景變化時具有更彊的魯棒性。
침대운동목표외관혹배경변화교대시,채용기우압축감지적근종산법유우특정단일역도치표이、근종불은정심지주실목표등문제,제출료개진적기우자괄응특정융합적압축감지근종산법。해산법채용량충수궤측량구진,분별투영V、H공간득도압축후적문리화안색특정,이용재선계산적특정가고성상대정도래자괄응조정특정가권계수,충분이용량류특정적호보성래증강근종은정성。대불동시빈적측시결과표명,제출적방법재목표외관、배경배경변화시잉능준학근종목표,재목표대소위70상소×100상소시평균정솔위22정/s,체도실시성。여제취단일특정적원압축감지산법상비,개진후적방법재목표외관화배경변화시구유경강적로봉성。
As real-time compressive tracking algorithm cannot steadily track object when appearance of target or external environment change heavily, compressive tracking method based on adaptive feature fusion is proposed. Two random measurement matrices are adopted to obtain texture and color features from V and H channel respectively. Then relative reliability index is used to calculate weights which can be update online, and two features are combined to improve tracking performance based on their own advantages. Testing results on challenging videos show that the proposed method can track object stably when target appearance or external environment change heavily. The method can run in real-time that the average frame rate is 22 frames/s when the target scale is 70 pixel×100 pixel. The proposed method is more robust in comparison with the compressive tracking algorithm which extracting single feature.