计算机工程
計算機工程
계산궤공정
COMPUTER ENGINEERING
2014年
3期
283-286,293
,共5页
压缩感知%目标跟踪%粒子滤波%结构化随机矩阵%梯度投影%基追踪
壓縮感知%目標跟蹤%粒子濾波%結構化隨機矩陣%梯度投影%基追蹤
압축감지%목표근종%입자려파%결구화수궤구진%제도투영%기추종
compressive sensing%object tracking%particle filtering%structured random matrices%gradient projection%basis pursuit
对现有基于压缩感知的视频目标跟踪系统进行改进，提出一种可实现隐私保护的监控视频目标跟踪系统。在编码端采用结构化随机矩阵，以提高随机采样矩阵的生成速度。在解码端采用GPSR-BB算法，以提高系统抗噪性。利用粒子滤波器算法实现目标跟踪，减少跟踪结果误差对压缩感知恢复算法准确性的影响和分析时间。实验结果表明，该系统在实现隐私保护的同时，提高了系统对光照的鲁棒性，在室内外光照条件下均能准确跟踪目标。与BP和Lasso方法相比，分别可节约30.3%和51.6%的处理时间。
對現有基于壓縮感知的視頻目標跟蹤繫統進行改進，提齣一種可實現隱私保護的鑑控視頻目標跟蹤繫統。在編碼耑採用結構化隨機矩陣，以提高隨機採樣矩陣的生成速度。在解碼耑採用GPSR-BB算法，以提高繫統抗譟性。利用粒子濾波器算法實現目標跟蹤，減少跟蹤結果誤差對壓縮感知恢複算法準確性的影響和分析時間。實驗結果錶明，該繫統在實現隱私保護的同時，提高瞭繫統對光照的魯棒性，在室內外光照條件下均能準確跟蹤目標。與BP和Lasso方法相比，分彆可節約30.3%和51.6%的處理時間。
대현유기우압축감지적시빈목표근종계통진행개진，제출일충가실현은사보호적감공시빈목표근종계통。재편마단채용결구화수궤구진，이제고수궤채양구진적생성속도。재해마단채용GPSR-BB산법，이제고계통항조성。이용입자려파기산법실현목표근종，감소근종결과오차대압축감지회복산법준학성적영향화분석시간。실험결과표명，해계통재실현은사보호적동시，제고료계통대광조적로봉성，재실내외광조조건하균능준학근종목표。여BP화Lasso방법상비，분별가절약30.3%화51.6%적처리시간。
The proposed system of privacy-enabled object tracking is improved based on the primary one. After using the structurally random matrices at the encoder, the generation speed of random sampling matrix is improved. After using fast methods such as GPSR-BB in reconstruction and particle filtering in analysis at the decoder, the noise resistance of system is improved. It uses particle filtering algorithm for target tracking, reduces the tracking results error influence on compression perception recovery algorithm accuracy, as well as the time needed for analysis steps. Experimental result shows that the proposed framework enables the privacy in tracking and at the same time increases the robustness to illumination condition. It also avoids the error of tracking results affecting the accuracy of CS reconstruction algorithm, which is faster than the original one and the performance of tracking is excellent. The process time of this system is saved 30.3%and 51.6%of the BP and Lasso method.
