智能视频监控系统中若干检测与跟踪算法的研究
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摘要
随着电子信息技术、计算机软硬件技术的不断发展,视频监控系统已经在城市化进程中体现出日益重要的价值,其应用早已渗透到政治、军事、文化、金融、科技等各个领域。智能算法作为视频监控系统的重要组成部分,已经在安防领域中发挥了巨大的作用。同时,智能算法也是视频监控系统相比其它形式的监控系统具有更高的性价比,更适合现代化发展的关键因素。因而,具有重要的研究意义。
本文围绕着智能视频监控系统中的若干应用,重点研究了火焰检测、人头检测及人体上半身检测与分割、目标跟踪等三个方面的问题。论文工作包括：
1.火焰检测方面,提出了一种新的基于人工神经网络的视频火焰检测方法。该方法在分析火焰的运动和颜色特征以外,还研究并利用了火焰的闪烁频率、几何形状等时空域特征,并用所获得的各类特征作为人工神经网络的输入,输出一个经过综合判断的结果。提出了针对快速傅立叶变换的GPU加速算法。所提方法能够区分闪烁的车灯与真实的火焰,并获得了较高的检测准确率。
2.人头检测及人体上半身检测与分割方面,分别提出了有向梯度直方图与二维形状直方图这两个特征。在此基础上,针对人头检测,进一步提出了基于贝叶斯决策论的运动与外观似然的滤波方法；针对人体上半身检测,进一步提出了结合能量函数最优化与背景剔除技术的前景对象分割方法。在计算有向梯度直方图特征上,提出一种基于CUDA的GPU加速算法。所提的人头检测方法有效地降低了检测的误检率,而人体上半身检测与分割方法则能够准确提取位于前景部分的人体区域。
3.目标跟踪方面,提出了一种基于推土机距离与SURF特征点的目标跟踪算法。提出了把解基于SURF特征点的跟踪问题规约到解推土机距离的线性规划问题上的思想。另外,提出了分两阶段由粗到精的跟踪方法和基于贝叶斯概率理论的多个目标对象发生遮挡时的处理方法。所提跟踪方法在实际视频监控系统中能够有效地对多个目标进行长时间跟踪,并具有较高的鲁棒性与可靠性。
本文所提出的三个视频监控系统智能算法经实验证明是可行的,并且其中的若干思想已经和实际的应用进行了结合,与现有的监控系统进行了集成,取得了一定的实用效果。
With development of informantion and computer hardware-software technologies, video surveillance system has revealed its importance in the urbanization process. The range of its applications includes various fields such as politics, military, culture, finance and technology. Specially, the application of computer vision algorithms in surveillance system is a crucial premise of its intellectualization. It is also the main reason of the superiority of video-based surveillance system compared with other surveillance system, which has shown higher cost performance and more appropriateness for modern development. In conclusion, the research to computer vision in surveillance system has very important significance.
In this paper, we choose several most representative intelligent applications in video surveillance field and carry on research about the algorithms to solve with them. It includes three aspects:fire detection, head detection and human upper body detection and segmentation, objects tracking. In details:
1. In the field of fire detection, we propose a novel video fire detection method based on artificial neural network. Except for analyzing fire's motion and color features, the proposed method researches and utilizes tempral and special features such as fire's flickering frequency and geometry. All these extracted features are fed into an artificial neural network and the network outputs an integrated result. Moreover, we propose GPU based fast Fourier transformation algorithm. The proposed method can distinguish between flickering vehicle light and real fire, which results higher detection rate.
2. In the field of head detection and human upper body detection and segmentation, we propose histogram of oriented gradient and shape2D histogram features respectively. On that basis, for head detection, we further propose filter method of motion and appearance likelihoods based on Bayesian theory. For human upper body detection and segmentation, we further propose foreground segmentation method combined background subtraction and energy function optimization. Moreover, we design a GPU acceleration algorithm based on CUDA in computing HOG feature. The proposed head detection method reduce false positives effectively while proposed human upper body detection and segmentationi method can achieve extraction of upper body regions correctly.
3. In the field of objects tracking, we propose an objects tracking method based earth mover's distance (EMD) and SURF feature points. We introduce the idea of reducing the problem of tracking objects with SURF feature points to the linear programming problem which solves EMD. Otherwise, we propose two phases tracking strategy, which means coarse-to-fine idea and the solution of multi-objects occlusion based on Bayesian framework. The proposed tracking method can locate multiple objects for a long time and achieve robustness and reliability.
Experiments have proven that the three proposed methods are available and have excellent performance. We have integrated them into real surveillance systems and achieved applicable results.
本文围绕着智能视频监控系统中的若干应用,重点研究了火焰检测、人头检测及人体上半身检测与分割、目标跟踪等三个方面的问题。论文工作包括：
1.火焰检测方面,提出了一种新的基于人工神经网络的视频火焰检测方法。该方法在分析火焰的运动和颜色特征以外,还研究并利用了火焰的闪烁频率、几何形状等时空域特征,并用所获得的各类特征作为人工神经网络的输入,输出一个经过综合判断的结果。提出了针对快速傅立叶变换的GPU加速算法。所提方法能够区分闪烁的车灯与真实的火焰,并获得了较高的检测准确率。
2.人头检测及人体上半身检测与分割方面,分别提出了有向梯度直方图与二维形状直方图这两个特征。在此基础上,针对人头检测,进一步提出了基于贝叶斯决策论的运动与外观似然的滤波方法；针对人体上半身检测,进一步提出了结合能量函数最优化与背景剔除技术的前景对象分割方法。在计算有向梯度直方图特征上,提出一种基于CUDA的GPU加速算法。所提的人头检测方法有效地降低了检测的误检率,而人体上半身检测与分割方法则能够准确提取位于前景部分的人体区域。
3.目标跟踪方面,提出了一种基于推土机距离与SURF特征点的目标跟踪算法。提出了把解基于SURF特征点的跟踪问题规约到解推土机距离的线性规划问题上的思想。另外,提出了分两阶段由粗到精的跟踪方法和基于贝叶斯概率理论的多个目标对象发生遮挡时的处理方法。所提跟踪方法在实际视频监控系统中能够有效地对多个目标进行长时间跟踪,并具有较高的鲁棒性与可靠性。
本文所提出的三个视频监控系统智能算法经实验证明是可行的,并且其中的若干思想已经和实际的应用进行了结合,与现有的监控系统进行了集成,取得了一定的实用效果。
With development of informantion and computer hardware-software technologies, video surveillance system has revealed its importance in the urbanization process. The range of its applications includes various fields such as politics, military, culture, finance and technology. Specially, the application of computer vision algorithms in surveillance system is a crucial premise of its intellectualization. It is also the main reason of the superiority of video-based surveillance system compared with other surveillance system, which has shown higher cost performance and more appropriateness for modern development. In conclusion, the research to computer vision in surveillance system has very important significance.
In this paper, we choose several most representative intelligent applications in video surveillance field and carry on research about the algorithms to solve with them. It includes three aspects:fire detection, head detection and human upper body detection and segmentation, objects tracking. In details:
1. In the field of fire detection, we propose a novel video fire detection method based on artificial neural network. Except for analyzing fire's motion and color features, the proposed method researches and utilizes tempral and special features such as fire's flickering frequency and geometry. All these extracted features are fed into an artificial neural network and the network outputs an integrated result. Moreover, we propose GPU based fast Fourier transformation algorithm. The proposed method can distinguish between flickering vehicle light and real fire, which results higher detection rate.
2. In the field of head detection and human upper body detection and segmentation, we propose histogram of oriented gradient and shape2D histogram features respectively. On that basis, for head detection, we further propose filter method of motion and appearance likelihoods based on Bayesian theory. For human upper body detection and segmentation, we further propose foreground segmentation method combined background subtraction and energy function optimization. Moreover, we design a GPU acceleration algorithm based on CUDA in computing HOG feature. The proposed head detection method reduce false positives effectively while proposed human upper body detection and segmentationi method can achieve extraction of upper body regions correctly.
3. In the field of objects tracking, we propose an objects tracking method based earth mover's distance (EMD) and SURF feature points. We introduce the idea of reducing the problem of tracking objects with SURF feature points to the linear programming problem which solves EMD. Otherwise, we propose two phases tracking strategy, which means coarse-to-fine idea and the solution of multi-objects occlusion based on Bayesian framework. The proposed tracking method can locate multiple objects for a long time and achieve robustness and reliability.
Experiments have proven that the three proposed methods are available and have excellent performance. We have integrated them into real surveillance systems and achieved applicable results.
引文
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