Multi-camera Tracking and Segmentation of Occluded People on Ground Plane Using Search-Guided Particle Filtering
A multi-view multi-hypothesis approach to segmenting and tracking multiple (possibly occluded) persons on a ground plane is proposed. During tracking, several iterations of segmentation are performed using information from human appearance models and ground plane homography. To more precisely locate the ground location of a person, all center vertical axes of the person across views are mapped to the top-view plane and their intersection point on the ground is estimated. To tackle the explosive state space due to multiple targets and views, iterative segmentation-searching is incorporated into a particle filtering framework. By searching for people's ground point locations from segmentations, a set of a few good particles can be identified, resulting in low computational cost. In addition, even if all the particles are away from the true ground point, some of them move towards the true one through the iterated process as long as they are located nearby. We demonstrate the performance of the approach on several video sequences.

title: Background modeling and subtraction by codebook construction
abstract: We present a new fast algorithm for background modeling and subtraction. Sample background values at each pixel are quantized into codebooks which represent a compressed form of background model for a long image sequence. This allows us to capture structural background variation due to periodic-like motion over a long period of time under limited memory. Our method can handle scenes containing moving backgrounds or illumination variations (shadows and highlights), and it achieves robust detection for compressed videos. We compared our method with other multimode modeling techniques.

title: Real-time foreground-background segmentation using codebook model
abstract: We present a real-time algorithm for foreground-background segmentation. Sample background values at each pixel are quantized into codebooks which represent a compressed form of background model for a long image sequence. This allows us to capture structural background variation due to periodic-like motion over a long period of time under limited memory. The codebook representation is efficient in memory and speed compared with other background modeling techniques. Our method can handle scenes containing moving backgrounds or illumination variations, and it achieves robust detection for different types of videos. We compared our method with other multimode modeling techniques. In addition to the basic algorithm, two features improving the algorithm are presented-layered modeling/detection and adaptive codebook updating. For performance evaluation, we have applied perturbation detection rate analysis to four background subtraction algorithms and two videos of different types of scenes.

title: Background Updating for Visual Surveillance
abstract: Scene changes such as moved objects, parked vehicles, or opened/closed doors need to be carefully handled so that interesting foreground targets can be detected along with the short-term background layers created by those changes. A simple layered modeling technique is embedded into a codebook-based background subtraction algorithm to update a background model. In addition, important issues related to background updating for visual surveillance are discussed. Experimental results on surveillance examples, such as unloaded packages and unattended objects, are presented by showing those objects as short-term background layers.

title: Efficient Construction of Pipelined Multibit-Trie Router-Tables
abstract: Efficient algorithms to construct multibit tries suitable for pipelined router-table applications are developed. We first enhance the 1-phase algorithm of Basu and Narlikar, obtaining a 1-phase algorithm that is 2.5 to 3 times as fast. Next, we develop 2-phase algorithms that not only guarantee to minimize the maximum per-stage memory but also guarantee to use the least total memory subject to the former constraint. Our 2-phase algorithms not only generate better pipelined trees than those generated by the 1-phase algorithm, but they also take much less time. A node pull-up scheme that guarantees no increase in maximum per-stage memory as well as a partitioning heuristic that generates pipelined multibit tries requiring less maximum per-stage memory than required by the tries obtained using the 1-phase and 2-phase algorithms are also proposed

title: TCP throughput analysis under transmission error and congestion losses
abstract: This paper analyzes the performance of a large population of long lived TCP flows experiencing random packet losses due to both random transmission errors and congestion created by the sharing of a common tail drop bottleneck router. We propose a natural and simple model for the joint throughput evolution of the set of TCP sessions under such a mix of losses. For the case of Poisson transmission errors, we show that the asymptotic model where the population tends to infinity leads to a well defined and tractable dynamical system. In particular, we get the mean value of the throughput of each session as a function of the transmission error rate and the synchronization rate in the bottleneck router. The large population asymptotic model has two interesting and non-intuitive properties: 1) there exists a positive threshold (given in closed form) on the transmission error rate above which there are no congestion losses at all in steady state; 2) below this threshold, the mean throughput of each flow is an increasing function of the transmission error rate, so that the maximum mean value is in fact achieved when the transmission error rate is equal to this threshold. The finite population model and models based on other classes of point processes are also studied. In particular, a sufficient condition is obtained for the existence of congestion times in the case of arbitrary transmission error point processes.

title: Analysis of the competition between wired, DSL and wireless users in an access network
abstract: This paper analyzes the performance of a large population composed of several classes of long lived TCP flows experiencing packet losses due to random transmission errors and to congestion created by the sharing of a common tail-drop or RED bottleneck router. Each class has a different transmission error rate. This setting is used to analyze the competition between wired and wireless users in an access network, where one class (the wired class) has no or small (like BER in DSL) transmission error losses whereas the other class has higher transmission error losses, or the competition between DSL flows using different coding schemes. We propose a natural and simple model for the joint throughput evolution of several classes of TCP flows under such a mix of losses. Two types of random transmission error losses are considered: one where losses are Poisson and independent of the rate of the flow, and one where the losses are still Poisson but with an intensity that is proportional to the rate of the source. We show that the large population model where the population tends to infinity has a threshold (given in closed form) below which there are no congestion losses at all in steady state, and above which there is a stationary limiting regime in which we can compute both the mean value and the distribution of the rate obtained by each class of flow. We also show that the maximum mean value for the aggregated rate is achieved at the threshold.

title: Efficient construction of variable-stride multibit tries for IP lookup
abstract: Srinivasan and Varghese (1999) have proposed the use of multibit tries to represent routing tables used for Internet (IP) address lookups. They propose an O(n /sub */W/sup 2//sub */ k) dynamic programming algorithm to determine the strides for an optimal variable-stride trie that has at most k levels. Here, n is the number of prefixes in the routing table and W is the length of the longest prefix. We improve on this algorithm by providing an alternative dynamic programming formulation. The complexity of our algorithm is O(n/sub */W/sub */k), on real router data sets. This is an improvement by a factor of W over the corresponding algorithm of Srinivasan. Experiments conducted by us indicate that our variable-stride algorithm is between 2 and 17 times as fast for IPv4 routing table data.

title: Iterative kernel principal component analysis for image modeling
abstract: In recent years, kernel principal component analysis (KPCA) has been suggested for various image processing tasks requiring an image model such as, e.g., denoising or compression. The original form of KPCA, however, can be only applied to strongly restricted image classes due to the limited number of training examples that can be processed. We therefore propose a new iterative method for performing KPCA, the kernel Hebbian algorithm, which iteratively estimates the kernel principal components with only linear order memory complexity. In our experiments, we compute models for complex image classes such as faces and natural images which require a large number of training examples. The resulting image models are tested in single-frame super-resolution and denoising applications. The KPCA model is not specifically tailored to these tasks; in fact, the same model can be used in super-resolution with variable input resolution, or denoising with unknown noise characteristics, in spite of this, both super-resolution and denoising performance are comparable to existing methods.