Research on the Traffic Event Discovery in Video Surveillance

视频监控系统的广泛运用，为人们在交通管理和安全监督提供了很大的便利，然而这种便利需要耗费巨大的人力物力去干预和监督。随着科学技术的发展，视频监控系统智能化成为解决该问题的研究方向，但是目前针对异常事件发现的视频监控系统智能化仍不足以满足人们的需求。本文在实验室前课题组研究智能视频监控技术的基础上，研究道路交通事件检测技术并构建了一个道路交通事件检测系统。本文的主要工作如下： (1)介绍视频处理中比较常用的运动目标检测方法并在不同场景下对检测效果进行比较，采用了效果较好的混合高斯模型。在阴影检测算法中，通过统计阴影区域像素在的变化用高斯分布进行建模，从而根据概率大小完成对阴影像素的判断。在对运...Video surveillance systems that are used widely can provide people with a great convenience in traffic management and safety oversight, however, this convenience takes enormous human and material resources to intervene and supervise. With the development of science and technology, intelligent video surveillance system is a good solution to solve that problem, but the intelligent video surveillance...学位：工学硕士院系专业：信息科学与技术学院_计算机科学与技术学号：2302013115315

Overlap of convex polytopes under rigid motion

We present an algorithm to compute a rigid motion that approximately maximizes the volume of the intersection of two convex polytopes P-1 and P-2 in R-3. For all epsilon is an element of (0, 1/2] and for all n >= 1/epsilon, our algorithm runs in O(epsilon(-3) n log(3.5) n) time with probability 1 - n(-O(1)). The volume of the intersection guaranteed by the output rigid motion is a (1 - epsilon)-approximation of the optimum, provided that the optimum is at least lambda . max{vertical bar P-1 vertical bar . vertical bar P-2 vertical bar} for some given constant lambda is an element of (0, 1]. (C) 2013 Elsevier B.V. All rights reserved.X1155Ysciescopu

Modeling and tracking relative movement of object parts

Video surveillance systems play an important role in many civilian and military applications, for the purposes of security and surveillance. Object detection is an important component in a video surveillance system, used to identify possible objects of interest and to generate data for tracking and analysis purposes. Not much exploration has been done to track the moving parts of the object which is being tracked. Some of the promising techniques like Kalman Filter, Mean-shift algorithm, Matching Eigen Space, Discrete Wavelet Transform, Curvelet Transform, Distance Metric Learning have shown good performance for keeping track of moving object. Most of this work is focused on studying and analyzing various object tracking techniques which are available. Most of the techniques which are available for object tracking have heavy computation requirements. The intention of this research is to design a technique, which is not computationally intensive and to be able to track relative movements of object parts in real time. The research applies a technique called foreground detection (also known as background subtraction) for tracking the object as it is not computationally intensive. For tracking the relative movement of object parts, a skeletonization technique is used. During implementation, it is found that using skeletonization technique, it is harder to extract the objects parts

Image enhancement using statistical spatial segmentation

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1996.Includes bibliographical references (leaves 56-58).by Peter Y. Yao.M.Eng

Target tracking and image interpretation in natural open world scenes

This thesis is concerned with tracking man made objects moving in natural open world scenes and based on the tracking data, construct a structural representation of that scene, frame by frame. The system developed uses a static camera and a statistical frame differencing technique for detecting motion in an image that has a relatively static background. Objects with a measured temporal consistency are tracked across successive image frames. Based on the tracking data, regions in the scene are associated with particular types of dynamic event. For example regions containing movement (could be roads) and regions where objects seem to disappear or partially disappear (could be hedges). Because of the sensitivity of the motion estimator to changes in scene illumination and environmental conditions, a tile-based method is used to detect scene motion based on the estimations of statistical variations within the tiles. An updating process is used to ensure that a reliable estimate of the background reference image is maintained by the system. Motion cues are matched against tracked objects from a previous frame using an estimate of the temporal continuity of an object. A spatial-temporal reasoning process is used to infer the structure in the image. This inference mechanism is implemented using a semantic network. The system has been tested on several open world sequences and in each case has demonstrated that it can identify and track vehicles moving in the scene. Based on the motion of these vehicles regions in the image were identified and scene maps constructed for each scene. The map identified regions where vehicles can be expected to be observed moving and regions where they could become occluded. A CD-ROM is included with this thesis that contains the results obtained by the system for the two image sequences used in chapter seven. These results incorporate some of the enhancements outlined in chapter 8, section 8.3. A windows movie player is included on the CD-ROM and appendix d provides information on the contents of the CD-ROM together with installation and operating instructions

Region-based tracking in an image sequence

This paper addresses the problem of motion tracking in a sequence of monocular images. We want to establish and maintain the successive positions of objects in a sequence of images. The use of regions as primitives for tracking enables us to directly handle consistent object-level entities. On one hand, a motion-based segmentation process based on normal flows and first order motion models provides us with instantaneous measurements of the geometry of each region present in the segmented images. Shape and position of each projected object are estimated with a recursive algorithm along the sequence. On the other hand, a motion filter based on a multiresolution estimation scheme and temporal filtering generates reliable estimates of the motion parameters of each region. The proposed approach relies on adequate modeling and measurement of the geometry and kinematics of object projections. In particular no 3-D information is required. It realizes a good trade-off between tractability and efficiency. Occlusion situations can be handled. We have carried out experiments on both sequences of synthetic and real images depicting complex outdoor scenes to illustrate the performance of this new method