本文選題：HEVC + 運動矢量�。� 參考：《江南大學》2017年碩士論文

【摘要】：目前對于交通事故的采樣和調(diào)查主要通過人工查看的方式,這種方法費時費力。近年來計算機視覺技術的發(fā)展為車輛異常事件的檢測提供了諸多解決方案,但都是基于像素域的方法。然而由于監(jiān)控視頻的高分辨和像素域方法的解碼特性,使得傳統(tǒng)像素域方法很難滿足實時性的要求。在實時性上壓縮域有著很大優(yōu)勢。通過對當前最新壓縮域編碼標準HEVC的研究和大量交通事故視頻的分析,發(fā)現(xiàn)HEVC包含的分塊模式和運動矢量可以看作是目標邊界和光流的粗糙分析,這些信息對于交通事件的檢測很有幫助。當前在壓縮域方向的研究主要是對運動目標的分割和跟蹤,這些壓縮域算法追求高分割精度而使得計算復雜度較高,且國內(nèi)外對于交通事件很少有研究。本文在研究HEVC編碼標準的基礎上,根據(jù)HEVC的編碼特性,提出一種根據(jù)運動矢量和塊劃分信息的運動強度值算法實現(xiàn)對運動目標的檢測,并在此基礎上提出一種多參數(shù)模糊邏輯算法檢測出車輛異常事件。本文的主要工作和創(chuàng)新點包括:(1)針對HEVC的編碼結(jié)構(gòu)和視頻內(nèi)容有很大相關性的特點,提出一種基于運動強度值的運動目標檢測方法。首先從HEVC壓縮碼流中提取出運動矢量和預測單元塊劃分信息,對運動矢量進行去噪和歸一化的預處理,使得運動矢量更加平穩(wěn)可靠,然后結(jié)合塊劃分信息計算出每一個最大編碼單元塊的運動強度值。最后通過設定的自適應閾值將運動強度值較小的單元塊濾除,可以得到運動劇烈的運動目標,實現(xiàn)本文算法中基于運動強度值的運動目標檢測。(2)當前像素域方向關于交通事故檢測算法主要依據(jù)質(zhì)心坐標距離差和時間差進行判斷,針對這些算法計算復雜度高且可靠性較差,本文通過對車輛所在區(qū)域面積和運動強度值變化的分析,提出一種多參數(shù)模糊邏輯理論來檢測車輛異常事件。首先利用廣度優(yōu)先搜索算法對含有運動強度值的最大編碼單元塊構(gòu)建連通區(qū)域,提取出連通區(qū)域的面積和其對應的運動強度值。其次根據(jù)面積梯度信息區(qū)分出不同車型和交通沖突場景。最后通過每一幀的運動強度值、面積梯度等級和運動強度離均差值構(gòu)成模糊邏輯理論的輸入?yún)?shù),根據(jù)車輛運動特征隸屬度函數(shù)判斷當前幀是否發(fā)生車輛異常事件。通過對25組交通事故視頻的實驗驗證,可以精準地檢測出其中22組交通事故,檢測率為88%,證明了本文算法的有效性和可靠性。
[Abstract]:At present, the sampling and investigation of traffic accidents are mainly done by manual inspection, which is time-consuming and laborious. In recent years, the development of computer vision technology has provided many solutions for vehicle anomaly detection, but all are based on pixel domain methods. However, because of the high resolution of surveillance video and the decoding characteristics of pixel domain method, the traditional pixel domain method is difficult to meet the real-time requirements. In real-time compression domain has a great advantage. Based on the research of the latest compression domain coding standard HEVC and the analysis of a large number of traffic accident videos, it is found that the block pattern and motion vector contained in HEVC can be regarded as the rough analysis of the target boundary and optical flow. This information is helpful for traffic incident detection. At present, the research on the direction of compressed domain is mainly on the segmentation and tracking of moving objects. These compressed domain algorithms pursue high segmentation accuracy and make the computation complexity higher, and there are few researches on traffic events at home and abroad. In this paper, based on the research of HEVC coding standard and according to the coding characteristics of HEVC, a motion intensity algorithm based on motion vector and block partition information is proposed to detect moving targets. On this basis, a multi-parameter fuzzy logic algorithm is proposed to detect the abnormal events of vehicles. The main work and innovations of this paper include: (1) aiming at the strong correlation between the coding structure and video content of HEVC, a motion target detection method based on motion intensity is proposed. Firstly, the motion vector and block partition information are extracted from the HEVC compressed bitstream, and the motion vector is de-noised and normalized, which makes the motion vector more stable and reliable. Then the motion intensity of each block is calculated with block partition information. Finally, by setting the adaptive threshold, the unit block with small motion intensity can be filtered out, and the moving object can be obtained. In this paper, the moving target detection based on motion intensity value is realized. (2) the current pixel direction detection algorithm for traffic accident detection is mainly based on the distance difference of centroid coordinates and time difference. In view of the high computational complexity and poor reliability of these algorithms, this paper proposes a multi-parameter fuzzy logic theory to detect the abnormal events of vehicles by analyzing the variation of the area and the intensity of motion in the vehicle area. Firstly, the breadth-first search algorithm is used to construct the connected region with the maximum coding unit block containing the motion intensity value, and the area of the connected region and its corresponding motion intensity value are extracted. Secondly, according to the area gradient information, different vehicle types and traffic conflict scenarios are distinguished. Finally, the input parameters of fuzzy logic theory are made up of the motion intensity value of each frame, the area gradient grade and the difference between the motion intensity and the average value. According to the membership function of the vehicle motion characteristics, the vehicle abnormal events can be judged in the current frame. Through the experimental verification of 25 sets of traffic accident videos, 22 groups of traffic accidents can be accurately detected, and the detection rate is 88%, which proves the validity and reliability of the proposed algorithm.
【學位授予單位】：江南大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：U491.31;TP391.41

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