Jean Meloche

Statistical tracking in video traffic surveillance

Applications that interpret video data need to track objects as they move in a scene. Tracking methods that estimate the state trajectories of objects as they change over time (e.g. Kalman filter) have difficulty as the number of objects and clutter increase. We present an alternative, called statistical tracking, that is based on the concept of network tomography. A scene is modeled as a network of interconnected regions. Statistical tracking estimates the number of trips made from one region to another based on inter-region boundary traffic counts accumulated over time. It is not necessary to track an object through a scene, just to determine when an object crosses a boundary, something that is generally easier than estimating a continuous trajectory. In achieving this simplicity, statistical tracking gives up the ability to determine an objects state as the motion occurs. Instead, it determines mean traffic intensities based on statistics accumulated over a period of time. In spite of this limitation, there are several applications for which statistical tracking is useful. We demonstrate the application of the method to a large sample of video traffic surveillance data. The method does not require any data association which has some important implications concerning personal privacy.

Multivariate L-estimation

In one dimension, order statistics and ranks are widely used because they form a basis for distribution free tests and some robust estimation procedures. In more than one dimension, the concept of order statistics and ranks is not clear and several definitions have been proposed in the last years. The proposed definitions are based on different concepts of depth. In this paper, we define a new notion of order statistics and ranks for multivariate data based on density estimation. The resulting ranks are invariant under affinc transformations and asymptotically distribution free. We use the corresponding order statistics to define a class of multivariate estimators of location that can be regarded as multivariate L-estimators. Under mild assumptions on the underlying distribution, we show the asymptotic normality of the estimators. A modification of the proposed estimates results in a high breakdown point procedure that can deal with patches of outliers. The main idea is to order the observations according to their likelihoodf(X1),...,f(Xn). If the densityf happens to be cllipsoidal, the above ranking is similar to the rankings that are derived from the various notions of depth. We propose to define a ranking based on a kernel estimate of the densityf. One advantage of estimating the likelihoods is that the underlying distribution does not need to have a density. In addition, because the approximate likelihoods are only used to rank the observations, they can be derived from a density estimate using a fixed bandwidth. This fixed bandwidth overcomes the curse of dimensionality that typically plagues density estimation in high dimension.

Robust plug-in bandwidth estimators in nonparametric regression

Abstract In this paper, we propose a robust bandwidth selection method for local M-estimates used in nonparametric regression. We study the asymptotic behavior of the resulting estimates. We use the results of a Monte Carlo study to compare the performance of various competitors for moderate samples sizes. It appears that the robust plug-in bandwidth selector we propose compares favorably to its competitors, despite the need to select a pilot bandwidth. The Monte Carlo study shows that the robust plug-in bandwidth selector is very stable and relatively insensitive to the choice of the pilot.

Assessing network readiness for IP telephony

Networked multimedia applications require stringent real-time QoS guarantees. Successful deployment of such applications closely depends on the performance of the underlying data network. The characteristics and the QoS requirements of these applications are different from traditional data applications. Hence, prior to deployment it is necessary to evaluate a network from a multimedia perspective. In this paper, we focus on IP Telephony and describe a framework for providing tools for IP Telephony readiness evaluation. This framework can be easily generalized to other multimedia applications. Our approach injects synthesized voice traffic and measures perceived end-to-end quality. We present a novel idea of relating voice quality metrics to the performance of data network devices. Following the proposed framework, we developed a prototype tool to evaluate a network and to identify problems, if any, prior to IP Telephony deployment. Our tool automatically discovers the topology of a given network, and collects and integrates network device performance and voice quality metrics. We describe the architecture of our tool and provide sample outputs from a network consisting of 129 devices.

Scalable network assessment for IP telephony

Multimedia applications such as IP telephony are among the applications that demand strict quality of service (QoS) guarantees from the underlying data network. At the predeployment stage it is critical to assess whether the network can handle the QoS requirements of IP telephony and fix problems that may prevent a successful deployment. In this paper we describe a technique for efficiently assessing network readiness for IP telephony. Our technique relies on understanding link level QoS behavior in a network from an IP telephony perspective. We use network topology and end-to-end measurements collected from the network in locating the sources of performance problems that may prevent a successful IP telephony deployment. We present an empirical study conducted on a real network spanning three geographically separated sites of an enterprise network. The empirical results indicate that our approach efficiently and accurately pinpoints links in the network incurring the most significant delay.

Binary restoration of thin objects in multidimensional imagery

We present a method for restoration of noisy tomographic images for detecting thin objects, such as explosives. Use of a weighted mean-square estimate optimizes the solution to place emphasis on the infrequent, but significant local structure associated with thin objects. Experimental results show successful restoration at very high noise levels.

Experiences with evaluating network QoS for IP telephony

Successful deployment of networked multimedia applications such as IP telephony depends on the performance of the underlying data network. QoS requirements of these applications are different from those of traditional data applications. For example, while IP telephony is very sensitive to delay and jitter, traditional data applications are more tolerant of these performance metrics. Consequently, assessing a network to determine whether it can accommodate the stringent QoS requirements of IP telephony becomes critical. We describe a technique for evaluating a network for IP telephony readiness. Our technique relies on the data collection and analysis support of our prototype tool, ExamiNet/spl trade/. It automatically discovers the topology of a given network and collects and integrates network device performance and voice quality metrics. We report the results of assessing the IP telephony readiness of a real network of 31 network devices (routers/switches) and 23 hosts via ExamiNet/spl trade/. Our evaluation identified links in the network that were over utilized to the point at which they could not handle IP telephony.

Measurement Techniques in On-Demand Overlays for Reliable Enterprise IP Telephony

Maintaining good quality of service for real-time applications like IP Telephony requires quick detection and reaction to network impairments. In this paper, we propose and study novel measurement techniques in ORBIT, which is a simple, easily deployable architecture that uses single-hop overlays implemented with intelligent endpoints and independent relays. The measurement techniques provide rapid detection and recovery of IP Telephony during periods of network trouble. We study our techniques via detailed simulations of several multi-site enterprise topologies of varying sizes and three typical fault models. We show that our proposed techniques can detect network impairments rapidly and rescue IP Telephony calls in sub-second intervals. We observed that all impacted calls were rescued with only a few relays in the network and the run-time overhead was low. Furthermore, the relay sites needed to be provisioned with minimal additional bandwidth to support the redirected calls.

Evaluation of statistical and multiple-hypothesis tracking for video traffic surveillance

Abstract. Conventional tracking methods encounter difficulties as the number of objects, clutter, and sensors increase, because of the requirement for data association. Statistical tracking, based on the concept of network tomography, is an alternative that avoids data association. It estimates the number of trips made from one region to another in a scene based on interregion boundary traffic counts accumulated over time. It is not necessary to track an object through a scene to determine when an object crosses a boundary. This paper describes statistical tracing and presents an evaluation based on the estimation of pedestrian and vehicular traffic intensities at an intersection over a period of 1 month. We compare the results with those from a multiple-hypothesis tracker and manually counted ground-truth estimates.

Path switching and call protection for reliable IP telephony

Application-layer path switching systems ensure the performance and reliability of real-time applications (such as IP Telephony) by exploiting inherent redundancies in the underlying IP network. These systems monitor quality throughout an IP Telephony call, rapidly detect performance problems and, if necessary, re-direct calls around problems and congestion in the network. Under normal network load conditions, these systems can easily find a feasible alternate path and restore call quality within sub-second timeframes. However, at high network load, finding alternate paths is challenging due to inherent inaccuracies in the techniques that attempt to measure an end-to-end path’s ability to accommodate additional calls. These inaccuracies may adversely impact established calls on the path and some of the calls may even oscillate back and forth between various alternate paths. In this paper, we propose some novel techniques that address these problems and ensure system stability and performance at high network load. We introduce the concept of Spatial Slow Start, which allows rapid selection of a feasible path from a large possible set. In addition, we help protect a path’s established calls from other switching calls by using intelligent back-off and probationary admission control techniques. We conducted extensive simulation studies to evaluate the performance of the proposed techniques. The results indicate that our techniques significantly reduce the number of oscillations and the amount of time that calls experience poor quality.