A common method for real-time segmentation of moving regions in image sequences involves "background subtraction", or thresholding the error between an estimate of the image without moving objects and the current image. The numerous approaches to this problem differ in the type of background model used and the procedure used to update the model. This paper discusses modeling each pixel as a mixture of Gaussians and using an on-line approximation to update the model. The Gaussian, distributions of the adaptive mixture model are then evaluated to determine which are most likely to result from a background process. Each pixel is classified based on whether the Gaussian distribution which represents it most effectively is considered part of the background model. This results in a stable, real-time outdoor tracker which reliably deals with lighting changes, repetitive motions from clutter, and long-term scene changes. This system has been run almost continuously for 16 months, 24 hours a day, through rain and snow.

/pdf/adaptive-background-mixture-models-for-real-time-tracking-1kbbz5ydwn.pdf

Adaptive background mixture models for real-time tracking

This article is about a curious phenomenon. Suppose we have a data matrix, which is the superposition of a low-rank component and a sparse component. Can we recover each component individuallyq We prove that under some suitable assumptions, it is possible to recover both the low-rank and the sparse components exactly by solving a very convenient convex program called Principal Component Pursuit; among all feasible decompositions, simply minimize a weighted combination of the nuclear norm and of the e1 norm. This suggests the possibility of a principled approach to robust principal component analysis since our methodology and results assert that one can recover the principal components of a data matrix even though a positive fraction of its entries are arbitrarily corrupted. This extends to the situation where a fraction of the entries are missing as well. We discuss an algorithm for solving this optimization problem, and present applications in the area of video surveillance, where our methodology allows for the detection of objects in a cluttered background, and in the area of face recognition, where it offers a principled way of removing shadows and specularities in images of faces.

/pdf/robust-principal-component-analysis-59kf1xh72d.pdf

Robust principal component analysis

The goal of this article is to review the state-of-the-art tracking methods, classify them into different categories, and identify new trends. Object tracking, in general, is a challenging problem. Difficulties in tracking objects can arise due to abrupt object motion, changing appearance patterns of both the object and the scene, nonrigid object structures, object-to-object and object-to-scene occlusions, and camera motion. Tracking is usually performed in the context of higher-level applications that require the location and/or shape of the object in every frame. Typically, assumptions are made to constrain the tracking problem in the context of a particular application. In this survey, we categorize the tracking methods on the basis of the object and motion representations used, provide detailed descriptions of representative methods in each category, and examine their pros and cons. Moreover, we discuss the important issues related to tracking including the use of appropriate image features, selection of motion models, and detection of objects.

/pdf/object-tracking-a-survey-3706dclxb5.pdf

Object tracking: A survey

Our goal is to develop a visual monitoring system that passively observes moving objects in a site and learns patterns of activity from those observations. For extended sites, the system will require multiple cameras. Thus, key elements of the system are motion tracking, camera coordination, activity classification, and event detection. In this paper, we focus on motion tracking and show how one can use observed motion to learn patterns of activity in a site. Motion segmentation is based on an adaptive background subtraction method that models each pixel as a mixture of Gaussians and uses an online approximation to update the model. The Gaussian distributions are then evaluated to determine which are most likely to result from a background process. This yields a stable, real-time outdoor tracker that reliably deals with lighting changes, repetitive motions from clutter, and long-term scene changes. While a tracking system is unaware of the identity of any object it tracks, the identity remains the same for the entire tracking sequence. Our system leverages this information by accumulating joint co-occurrences of the representations within a sequence. These joint co-occurrence statistics are then used to create a hierarchical binary-tree classification of the representations. This method is useful for classifying sequences, as well as individual instances of activities in a site.

/pdf/learning-patterns-of-activity-using-real-time-tracking-4pcdy8vvvh.pdf

Learning patterns of activity using real-time tracking

Networked sensors-those that coordinate amongst themselves to achieve a larger sensing task-will revolutionize information gathering and processing both in urban environments and in inhospitable terrain. The sheer numbers of these sensors and the expected dynamics in these environments present unique challenges in the design of unattended autonomous sensor networks. These challenges lead us to hypothesize that sensor network coordination applications may need to be structured differently from traditional network applications. In particular, we believe that localized algorithms (in which simple local node behavior achieves a desired global objective) may be necessary for sensor network coordination. In this paper, we describe localized algorithms, and then discuss directed diffusion, a simple communication model for describing localized algorithms.

https://www.cs.jhu.edu/~cs647/class-papers/Sensor%20Nets/estrin.pdf

Next century challenges: scalable coordination in sensor networks

We describe a vision system that monitors activity in a site over extended periods of time. The system uses a distributed set of sensors to cover the site, and an adaptive tracker detects multiple moving objects in the sensors. Our hypothesis is that motion tracking is sufficient to support a range of computations about site activities. We demonstrate using the tracked motion data to calibrate the distributed sensors, to construct rough site models, to classify detected objects, to learn common patterns of activity for different object classes, and to detect unusual activities.

/pdf/using-adaptive-tracking-to-classify-and-monitor-activities-isfadd20bj.pdf

Using adaptive tracking to classify and monitor activities in a site

This paper describes an automatic surveillance system, which performs labeling of events and interactions in an outdoor environment. The system is designed to monitor activities in an open parking lot. It consists of three components-an adaptive tracker, an event generator, which maps object tracks onto a set of pre-determined discrete events, and a stochastic parser. The system performs segmentation and labeling of surveillance video of a parking lot and identifies person-vehicle interactions, such as pick-up and drop-off. The system presented in this paper is developed jointly by MIT Media Lab and MIT Artificial Intelligence Lab.

/pdf/video-surveillance-of-interactions-246id87xvw.pdf

Video surveillance of interactions

This paper introduces a method for robustly estimating a planar tracking correspondence model (TCM) for a large camera network directly from tracking data and for employing said model to reliably track objects through multiple cameras. By exploiting the unique characteristics of tracking data, our method can reliably estimate a planar TCM in large environments covered by many cameras. It is robust to scenes with multiple simultaneously moving objects and limited visual overlap between the cameras. Our method introduces the capability of automatic calibration of large camera networks in which the topology of camera overlap is unknown and in which all cameras do not necessarily overlap. Quantitative results are shown for a five camera network in which the topology is not specified.

Chris Stauffer

Papers

Adaptive background mixture models for real-time tracking

Learning patterns of activity using real-time tracking

Using adaptive tracking to classify and monitor activities in a site

Video surveillance of interactions

Automated multi-camera planar tracking correspondence modeling