基于深度相機人臉與行人感知系統(tǒng)的設(shè)計與實現(xiàn)
發(fā)布時間：2018-10-11 10:18
【摘要】：計算機視覺是人工智能的重要研究領(lǐng)域,目標檢測作為計算機視覺的基礎(chǔ)任務(wù),是學術(shù)界和工業(yè)界的研究熱點。其中,關(guān)于人的感知更是具有廣泛的應(yīng)用意義,尤其是在智能安防、無人駕駛和移動機器人等行業(yè)。在這些行業(yè)的解決方案中很多使用深度相機進行人的感知,以達到快速準確以及三維定位的目的。在一些商業(yè)化及開源代碼中,一般只針對單一設(shè)備或特定場景,并且難以根據(jù)用戶需求進行二次開發(fā)或功能擴展與刪減�；谝陨显�,本文提出基于深度相機,易于擴展且方便開發(fā)的人臉與行人感知系統(tǒng)。本系統(tǒng)主要分為四個模塊:硬件層、驅(qū)動層、應(yīng)用層、可視化層。層間,層內(nèi)的功能單元相互獨立,接口格式統(tǒng)一,易于調(diào)用,方便功能單元以插件形式擴展與刪減;硬件層兼容異構(gòu)的相機設(shè)備,包括多種深度相機,彩色相機;驅(qū)動層統(tǒng)一相機的接口;應(yīng)用層內(nèi)的某個單元可以方便被其他單元調(diào)用,例如檢測單元可為跟蹤單元服務(wù);可視化層使用機器人操作系統(tǒng)的3D可視化工具,能夠以多種顯示方式查看結(jié)果。本系統(tǒng)針對相機個數(shù)可分為單深度相機系統(tǒng)與多深度相機系統(tǒng),前者的感知應(yīng)用包括人臉檢測與識別,行人檢測與跟蹤;后者克服單深度相機系統(tǒng)覆蓋面小的缺點,組成相機網(wǎng)絡(luò),實現(xiàn)對行人的跨區(qū)域長時間的跟蹤。本系統(tǒng)針對人臉感知集成了快速人臉檢測與識別算法,方便部署于低功耗設(shè)備。對于RGB相機,集成Dlib人臉檢測器。對于深度相機,本文提出了基于Dlib訓練器并聯(lián)合RGB-D信息進行人臉(頭)檢測的方法,可使用深度相機準確檢測人臉(頭)。人臉識別模塊使用特征臉與費希爾臉的方法。對于行人感知,本系統(tǒng)使用了傳統(tǒng)的基于RGB-D的算法和基于多模態(tài)深度學習模型的方法,前者使用強大的三維圖像處理庫PCL進行開發(fā),后者基于當前快速而高效的faster R-CNN框架。本系統(tǒng)的跟蹤模塊利用Tracking-by-Detection的思想,并使用擴展卡爾曼濾波的方法以達到抗遮擋的效果。最后,本系統(tǒng)使用多個深度相機組成網(wǎng)絡(luò),使用相機標定的方法使得每個相機知道其它相機以及地面的位置,從而重構(gòu)相機所覆蓋到的三維世界,實現(xiàn)跨區(qū)域長時間的行人跟蹤。
[Abstract]:Computer vision is an important research field of artificial intelligence. As a basic task of computer vision, target detection is a hot research topic in academia and industry. Among them, human perception has a wide range of applications, especially in intelligent security, unmanned and mobile robots and other industries. In many solutions in these industries, depth cameras are used for human perception to achieve rapid, accurate and three-dimensional positioning. In some commercial and open source code, it is generally only for a single device or a specific scenario, and it is difficult to carry out secondary development or functional expansion and deletion according to user requirements. For the above reasons, this paper proposes a face and pedestrian perception system based on depth camera, which is easy to expand and develop. The system is mainly divided into four modules: hardware layer, driver layer, application layer, visual layer. Among the layers, the function units in the layers are independent of each other, the interface format is uniform, easy to call, the function units are easily extended and deleted in the form of plug-in, the hardware layer is compatible with heterogeneous camera equipment, including various depth cameras and color cameras; The driver layer unifies the camera interface; one unit in the application layer can be easily invoked by other units, such as the detection unit, which serves the tracking unit; the visualization layer uses the 3D visualization tool of the robot operating system. Ability to view results in multiple displays. According to the number of cameras, the system can be divided into single depth camera system and multi-depth camera system. The former includes face detection and recognition, pedestrian detection and tracking, and the latter overcomes the shortcomings of small coverage of single depth camera system. A camera network is formed to track pedestrians across areas for a long time. This system integrates fast face detection and recognition algorithms for face perception, which is convenient to deploy in low power equipment. For RGB camera, Dlib face detector is integrated. For the depth camera, this paper presents a method of face (head) detection based on Dlib trainer and RGB-D information, which can accurately detect the face (head) by using the depth camera. The method of feature face and Fisher face is used in face recognition module. For pedestrian perception, the system uses the traditional algorithm based on RGB-D and the method based on multi-modal depth learning model. The former is developed using a powerful 3D image processing library PCL, and the latter is based on the current fast and efficient faster R-CNN framework. The tracking module of this system uses the idea of Tracking-by-Detection and the method of extended Kalman filter to achieve the effect of anti-occlusion. Finally, the system uses multiple depth cameras to form a network, using camera calibration method to make each camera know the location of the other cameras and the ground, so as to reconstruct the 3D world covered by the camera. Long time pedestrian tracking across areas is achieved.
【學位授予單位】：浙江大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41
[Abstract]:Computer vision is an important research field of artificial intelligence. As a basic task of computer vision, target detection is a hot research topic in academia and industry. Among them, human perception has a wide range of applications, especially in intelligent security, unmanned and mobile robots and other industries. In many solutions in these industries, depth cameras are used for human perception to achieve rapid, accurate and three-dimensional positioning. In some commercial and open source code, it is generally only for a single device or a specific scenario, and it is difficult to carry out secondary development or functional expansion and deletion according to user requirements. For the above reasons, this paper proposes a face and pedestrian perception system based on depth camera, which is easy to expand and develop. The system is mainly divided into four modules: hardware layer, driver layer, application layer, visual layer. Among the layers, the function units in the layers are independent of each other, the interface format is uniform, easy to call, the function units are easily extended and deleted in the form of plug-in, the hardware layer is compatible with heterogeneous camera equipment, including various depth cameras and color cameras; The driver layer unifies the camera interface; one unit in the application layer can be easily invoked by other units, such as the detection unit, which serves the tracking unit; the visualization layer uses the 3D visualization tool of the robot operating system. Ability to view results in multiple displays. According to the number of cameras, the system can be divided into single depth camera system and multi-depth camera system. The former includes face detection and recognition, pedestrian detection and tracking, and the latter overcomes the shortcomings of small coverage of single depth camera system. A camera network is formed to track pedestrians across areas for a long time. This system integrates fast face detection and recognition algorithms for face perception, which is convenient to deploy in low power equipment. For RGB camera, Dlib face detector is integrated. For the depth camera, this paper presents a method of face (head) detection based on Dlib trainer and RGB-D information, which can accurately detect the face (head) by using the depth camera. The method of feature face and Fisher face is used in face recognition module. For pedestrian perception, the system uses the traditional algorithm based on RGB-D and the method based on multi-modal depth learning model. The former is developed using a powerful 3D image processing library PCL, and the latter is based on the current fast and efficient faster R-CNN framework. The tracking module of this system uses the idea of Tracking-by-Detection and the method of extended Kalman filter to achieve the effect of anti-occlusion. Finally, the system uses multiple depth cameras to form a network, using camera calibration method to make each camera know the location of the other cameras and the ground, so as to reconstruct the 3D world covered by the camera. Long time pedestrian tracking across areas is achieved.
【學位授予單位】：浙江大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41
【相似文獻】
相關(guān)期刊論文 前10條
1 張R，
本文編號：2263826
本文鏈接：http://sikaile.net/kejilunwen/ruanjiangongchenglunwen/2263826.html
最近更新
教材專著
