本文選題：智能監(jiān)控系統(tǒng) + Hu矩�。� 參考：《西南科技大學(xué)》2016年碩士論文

【摘要】：智能監(jiān)控系統(tǒng)因其全天候、無間斷、低誤報(bào)實(shí)時(shí)監(jiān)控的優(yōu)點(diǎn)而廣受關(guān)注,其中的目標(biāo)檢測(cè)、目標(biāo)跟蹤和行為識(shí)別等關(guān)鍵技術(shù)是學(xué)者們研究的熱點(diǎn)。針對(duì)室內(nèi)固定場(chǎng)景,深入研究了目標(biāo)檢測(cè)、目標(biāo)跟蹤與行為識(shí)別技術(shù),并分別針對(duì)存在的問題做出了改進(jìn)。目標(biāo)檢測(cè)部分,將基于Vi Be的背景差法與幀差法融合,通過判斷是否發(fā)生光照變化來選擇對(duì)當(dāng)前幀圖像進(jìn)行目標(biāo)檢測(cè)的方法,解決了Vi Be算法在光照變化的情況下檢測(cè)到的運(yùn)動(dòng)目標(biāo)不準(zhǔn)確的問題。目標(biāo)跟蹤部分,利用卡爾曼濾波器的運(yùn)動(dòng)估計(jì)來改進(jìn)Camshift目標(biāo)跟蹤算法,通過Bhattacharyya距離和遮擋率來判斷目標(biāo)是否被遮擋以及被遮擋的程度,能夠有效解決目標(biāo)在發(fā)生遮擋時(shí)跟蹤不穩(wěn)定的問題。異常行為識(shí)別部分,提出一種改進(jìn)的基于模板匹配的人體目標(biāo)異常行為識(shí)別算法,將改進(jìn)的Hu不變矩和圖像運(yùn)動(dòng)特征結(jié)合組成行為特征向量,采用Hausdorff距離計(jì)算待測(cè)行為特征向量與模板之間的相似性,并通過相應(yīng)的閾值判定待測(cè)行為是否屬于異常行為。實(shí)驗(yàn)結(jié)果表明改進(jìn)目標(biāo)檢測(cè)、跟蹤和識(shí)別算法均可行有效,并且提高了異常行為的識(shí)別率。
[Abstract]:Intelligent monitoring system has attracted much attention because of its advantages of all-weather, uninterrupted, low-false alarm real-time monitoring. The key technologies such as target detection, target tracking and behavior recognition are the research focus of scholars. The techniques of target detection, target tracking and behavior recognition are deeply studied for indoor fixed scenes, and the existing problems are improved respectively. In the object detection part, the background difference method based on Vi be and the frame difference method are fused to select the target detection method for the current frame image by judging whether the illumination changes or not. The problem of inaccurate moving target detected by Vi be algorithm is solved. In the part of target tracking, the motion estimation of Kalman filter is used to improve the Camshift target tracking algorithm, and the Bhattacharyya distance and occlusion rate are used to judge whether the target is occluded and the degree of occlusion. It can effectively solve the problem of tracking instability when occlusion occurs. In the part of abnormal behavior recognition, an improved algorithm based on template matching is proposed. The improved Hu invariant moment and image motion feature are combined to form the behavior feature vector. Hausdorff distance is used to calculate the similarity between the feature vector and the template of the behavior to be tested, and the corresponding threshold value is used to determine whether the behavior under test belongs to abnormal behavior. The experimental results show that the improved target detection, tracking and recognition algorithms are effective and the recognition rate of abnormal behavior is improved.
【學(xué)位授予單位】：西南科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP391.41
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本文編號(hào)：1974959