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

【摘要】：智能監(jiān)控系統(tǒng)因其全天候、無間斷、低誤報實時監(jiān)控的優(yōu)點而廣受關注,其中的目標檢測、目標跟蹤和行為識別等關鍵技術是學者們研究的熱點。針對室內固定場景,深入研究了目標檢測、目標跟蹤與行為識別技術,并分別針對存在的問題做出了改進。目標檢測部分,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當前幀圖像進行目標檢測的方法,解決了Vi Be算法在光照變化的情況下檢測到的運動目標不準確的問題。目標跟蹤部分,利用卡爾曼濾波器的運動估計來改進Camshift目標跟蹤算法,通過Bhattacharyya距離和遮擋率來判斷目標是否被遮擋以及被遮擋的程度,能夠有效解決目標在發(fā)生遮擋時跟蹤不穩(wěn)定的問題。異常行為識別部分,提出一種改進的基于模板匹配的人體目標異常行為識別算法,將改進的Hu不變矩和圖像運動特征結合組成行為特征向量,采用Hausdorff距離計算待測行為特征向量與模板之間的相似性,并通過相應的閾值判定待測行為是否屬于異常行為。實驗結果表明改進目標檢測、跟蹤和識別算法均可行有效,并且提高了異常行為的識別率。
[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.
【學位授予單位】：西南科技大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TP391.41
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本文編號：1974959