Detecting moving objects using the rigidity constraint

A method for visually detecting moving objects from a moving camera using point correspondences in two orthographic views is described. The method applies a simple structure-from-motion analysis and then identifies those points inconsistent with the interpretation of the scene as a single rigid object. It is effective even when the actual motion parameters cannot be recovered. Demonstrations are presented using point correspondences automatically determined from real image sequences     

Detecting moving objects, ghosts, and shadows in video streams

Background subtraction methods are widely exploited for moving object detection in videos in many applications, such as traffic monitoring, human motion capture, and video surveillance. How to correctly and efficiently model and update the background model and how to deal with shadows are two of the most distinguishing and challenging aspects of such approaches. The article proposes a general-purpose method that combines statistical assumptions with the object-level knowledge of moving objects, apparent objects (ghosts), and shadows acquired in the processing of the previous frames. Pixels belonging to moving objects, ghosts, and shadows are processed differently in order to supply an object-based selective update. The proposed approach exploits color information for both background subtraction and shadow detection to improve object segmentation and background update. The approach proves fast, flexible, and precise in terms of both pixel accuracy and reactivity to background changes.     

Detecting moving objects from a video taken by a moving camera using sequential inference of background images

This paper proposes a method of detecting moving objects using sequential inference of the background in a video taken with a moving camera. In the video taken using a moving camera, all positions of pixels change every frame. The positions of the background pixels in the image frame T are not the same as the positions of the background pixels in the image frame T + 1. 2D projective transform can be used to find changes in the pixel position every frame. Bilinear interpolation with four nearest pixels around the pixel in image frame T which corresponds to a pixel in the image frame T+1 can be used for creating a background model at T + 1. Having obtained the background model, a pixel in image frame T + 1 can be determined if it is a background pixel or a foreground pixel. The detection results of the proposed method are compared with the ground truth to determine the effectiveness of the proposed method.     

On multiple moving objects

This paper explores the motion-planning problem for multiple moving objects. The approach taken consists of assigning priorities to the objects, then planning motions one object at a time. For each moving object, the planner constructs a configuration space-time that represents the time-varying constraints imposed on the moving object by the other moving and stationary objects. The planner represents this space-time approximately, using two-dimensional slices. The space-time is then searched for a collision-free path. The paper demonstrates this approach in two domains. One domain consists of translating planar objects; the other domain consists of two-link planar articulated arms.     

On multiple moving objects

This paper explores the motion-planning problem for multiple moving objects. The approach taken consists of assigning priorities to the objects, then planning motions one object at a time. For each moving object, the planner constructs a configuration space-time that represents the time-varying constraints imposed on the moving object by the other moving and stationary objects. The planner represents this space-time approximately, using two-dimensional slices. The space-time is then searched for a collision-free path. The paper demonstrates this approach in two domains. One domain consists of translating planar objects; the other domain consists of two-link planar articulated arms.This report describes research performed at the Artificial Intelligence Laboratory of the Massachusetts Institute of Technology. Michael Erdmann is supported in part by a fellowship from General Motors Research Laboratories. Tomás Lozano-Pérez is supported by an NSF Presidential Young Investigator grant. Support for the Laboratory's Artificial Intelligence research is provided in part by the System Development Foundation, in part by the Office of Naval Research under Office of Naval Research Contract N00014-81-K-0494, and in part by the Advanced Research Projects Agency under Office of Naval Research Contracts N00014-80-C-0505 and N00014-82-K-0344.     

Processing moving queries over moving objects using motion-adaptive indexes

This paper describes a motion-adaptive indexing scheme for efficient evaluation of moving continual queries (MCQs) over moving objects. It uses the concept of motion-sensitive bounding boxes (MSBs) to model moving objects and moving queries. These bounding boxes automatically adapt their sizes to the dynamic motion behaviors of individual objects. Instead of indexing frequently changing object positions, we index less frequently changing object and query MSBs, where updates to the bounding boxes are needed only when objects and queries move across the boundaries of their boxes. This helps decrease the number of updates to the indexes. More importantly, we use predictive query results to optimistically precalculate query results, decreasing the number of searches on the indexes. Motion-sensitive bounding boxes are used to incrementally update the predictive query results. Furthermore, we introduce the concepts of guaranteed safe radius and optimistic safe radius to extend our motion-adaptive indexing scheme to evaluating moving continual k-nearest neighbor (kNN) queries. Our experiments show that the proposed motion-adaptive indexing scheme is efficient for the evaluation of both moving continual range queries and moving continual kNN queries.     

面向动摄像机的高速运动目标检测

目的为解决动摄像机中高速运动目标检测复杂度高的问题,提出一种基于压缩视频运动矢量的高速运动目标检测新方法。方法该方法首先分析监控视频的码流格式和解码特点;然后从视频流中直接提取运动矢量;接着进行运动矢量规范化,并根据3σ准则提取场景的全局运动参数;最后通过对运动矢量统计特征的分析,实现面向动摄像机的高速运动目标快速检测。结果仿真实验表明,该方法在经典和自建数据库上目标提取效率较现有算法均有较大提高。结论本文方法充分利用了压缩视频数据中蕴含的运动信息,极大降低运动目标检测的复杂度,可以有效提取动摄像机成像画面中的高速运动目标,在经典和自建数据库上的目标提取效率较现有算法均有较大提高。     

Influential facilities placement over moving objects

Distributed and Parallel Databases - In this paper we propose and study the problem of k-Collective influential facility placement over moving object. Specifically, given a set of candidate...     

移动对象数据库索引研究综述

对近年来移动对象索引技术的发展进行了总结。根据移动对象索引空间的不同,将移动对象索引分为无限制空间移动对象索引以及网络空间移动对象索引,分析了过去、现在、未来移动对象索引技术的发展情况,最后对移动对象索引未来发展方向进行了讨论。     

移动对象轨迹更新体系

在移动对象数据库中存在着移动对象位置的频繁更新,因而,如何管理移动对象轨迹（位置）的更新将成为移动对象数据库要解决的问题。对移动对象轨迹更新策略中存在的问题,提出了一个统一的移动对象轨迹更新体系。首先定义了移动对象的轨迹,从移动对象轨迹建模的角度,将移动对象轨迹的更新策略归纳为基于点与矢量的移动对象轨迹更新策略,给出了移动对象轨迹的更新原理及算法,并进行了两种更新策略的模拟实验比较。     

Camera calibration with moving one-dimensional objects

In this paper, we show that the rotating 1D calibrating object used in the literature is in essence equivalent to a familiar 2D planar calibration object. In addition, we also show that when the 1D object undergoes a planar motion rather than rotating around a fixed point, such equivalence still holds but the traditional way fails to handle it. Experiments are carried out to verify the theoretical correctness and numerical robustness of our results.     

Approximating CSG trees of moving objects

A discussion of the relationship between two solid representation schemes is presented: CSG trees and recursive spatial subdivision exemplified by the bintree, a generalization of the quadtree and octree. Detailed algorithms are developed and analyzed for evaluating CSG trees by bintree conversion. These techniques are shown to enable the addition of the time dimension and motion to the approximate analysis of CSG trees. This facilitates the solution of problems such as static and dynamic interference detection. A technique for projecting across any dimension is also shown. For “well-behaved” CSG trees the execution time of the conversion algorithm is directly related to the spatial complexity of the object represented by the CSG tree (i.e., as the resolution increases, it is asymptotically proportional to the number of bintree nodes and does not depend on the size or form of the CSG tree representation). The set of well-behaved CSG trees include all trees that define multidimensional polyhedra in a manner that does not give rise to tangential intersections at CSG tree nodes. This is an expanded version of a paper titled “Bintrees, CSG Trees, and Time” which appeared inProceedings of the SIGGRAPH '85 Conference, San Francisco (July 1985), pp. 121–130. This work was supported in part by the National Science Foundation under Grants DCR-83-02118 and IRI-88-02457 and in part by the Finnish Academy Deceased on August 5, 1989     

Pursuit problems with two moving objects

A discrete-time and discrete-phase-space model is constructed describing how one moving object pursues a target located in a given region in the Euclidean space. Numerical search algorithms are developed.Translated from Kibernetika, No. 4, pp. 37–42, July–August, 1989.     

Detecting textured objects using convex hull

In this paper, we present a methodology of locating 3D objects of known shapes from a single gray-scale image, in particular objects with rich textures on the surface. While traditional approaches identify objects by grouping and matching local features, we locate the object in the image using its convex hull, a high level feature not given much attention in the image using literature. A “direct line detection” algorithm is developed to detect line segments directly from the gray-scale image divided in small blocks. Lines are clustered and convex hull of a single or group of clusters is computed and edited to extract the 2D contour of the object. Successful experiments on rectangular boxes and cylinders show the effectiveness of the convex hull approach and its potential usage in industrial applications. Part of the work discussed in this paper was performed when both authors were affiliated with Symbol Technologies.     

Recognition of moving objects using feature signatures

An important application of machine vision systems is the recognition of known three-dimensional objects. A major difficulty arises when two or more objects project the same or similar two-dimensional image, often resulting in misclassification and degradation of system performance. The changes in images which result from the motion of objects provide a source of three-dimensional information which can greatly aid the classification process, but this three-dimensional analysis is computationally complex and subject to many sources of error. This work develops a methodology which utilizes the information derived from the apparent changes in object features over time to facilitate the recognition task, without the need to actually recover the three-dimensional structure of the objects under view. The basic approach is to generate a ``feature signature' by combining the feature measurements of the individual regions in a long sequence of images. The static information in the individual frames is analyzed along with the temporal information from the entire sequence. These techniques are particularly applicable in situations where static image processing methods cannot discriminate between ambiguous objects. Two example implementations are presented to illustrate the application of the techniques of object recognition using motion information.     

Computer tracking of objects moving in space

A method is developed to represent movement of convex blocks in three-dimensional space from a sequence of two-dimensional camera images. The goals are to determine the objects' movement toward or away from the camera as well as left/right and up/down movement in the image plane and to build models of the blocks. The movement information is used as part of a hierarchical matching process that determines the correspondence of blocks between scenes.     

Modeling and querying moving objects in networks

Moving objects databases have become an important research issue in recent years. For modeling and querying moving objects, there exists a comprehensive framework of abstract data types to describe objects moving freely in the 2D plane, providing data types such as moving point or moving region. However, in many applications people or vehicles move along transportation networks. It makes a lot of sense to model the network explicitly and to describe movements relative to the network rather than unconstrained space, because then it is much easier to formulate in queries relationships between moving objects and the network. Moreover, such models can be better supported in indexing and query processing. In this paper, we extend the ADT approach by modeling networks explicitly and providing data types for static and moving network positions and regions. In a highway network, example entities corresponding to these data types are motels, construction areas, cars, and traffic jams. The network model is not too simplistic; it allows one to distinguish simple roads and divided highways and to describe the possible traversals of junctions precisely. The new types and operations are integrated seamlessly into the ADT framework to achieve a relatively simple, consistent and powerful overall model and query language for constrained and unconstrained movement.     

Tracking moving objects during low altitude flight

A computer-vision system to assist pilots during low-altitude flight has been developed in this research study. During this critical section of flight, a system that can detect various objects on the ground would be very useful both for enhancing the safety of navigation and for relieving pilots of a part of their workload in flight control. Such tasks can generally be automated by computer-vision-based methods, which provide the ability for object detection and tracking. This paper describes the algorithms developed for accomplishing such tasks. There are two main stages in the vision system. First, independently moving objects in the scene are detected and segmented from the background. Then, they are tracked from frame to frame, and their 3D motion parameters are recursively estimated with Kalman filtering techniques. Experiments using real-world image sequences have been carried out, and the results show that tracking moving objects is successful and the estimation of object's motion parameters are quite accurate.