text: This paper addresses the problem of human re-identification across non-overlapping cameras in crowds.Re-identification in crowded scenes is a challenging problem due to large number of people and frequent occlusions, coupled with changes in their appearance due to different properties and exposure of cameras. To solve this problem, we model multiple Personal, Social and Environmental (PSE) constraints on human motion across cameras. The personal constraints include appearance and preferred speed of each individual assumed to be similar across the non-overlapping cameras. The social influences (constraints) are quadratic in nature, i.e. occur between pairs of individuals, and modeled through grouping and collision avoidance. Finally, the environmental constraints capture the transition probabilities between gates (entrances exits) in different cameras, defined as multi-modal distributions of transition time and destination between all pairs of gates. We incorporate these constraints into an energy minimization framework for solving human re-identification. Assigning @math correspondence while modeling PSE constraints is NP-hard. We present a stochastic local search algorithm to restrict the search space of hypotheses, and obtain @math solution in the presence of linear and quadratic PSE constraints. Moreover, we present an alternate optimization using Frank-Wolfe algorithm that solves the convex approximation of the objective function with linear relaxation on binary variables, and yields an order of magnitude speed up over stochastic local search with minor drop in performance. We evaluate our approach using Cumulative Matching Curves as well @math assignment on several thousand frames of Grand Central, PRID and DukeMTMC datasets, and obtain significantly better results compared to existing re-identification methods.
  @cite_30: This paper proposes an adaptive learning method for tracking targets across multiple cameras with disjoint views. Two visual cues are usually employed for tracking targets across cameras: spatio-temporal cue and appearance cue. To learn the relationships among cameras, traditional methods used batch-learning procedures or hand-labeled correspondence, which can work well only within a short period of time. In this paper, we propose an unsupervised method which learns both spatio-temporal relationships and appearance relationships adaptively and can be applied to long-term monitoring. Our method performs target tracking across multiple cameras while also considering the environment changes, such as sudden lighting changes. Also, we improve the estimation of spatio-temporal relationships by using the prior knowledge of camera network topology.
  @cite_62: Egocentric cameras are becoming increasingly popular and provide us with large amounts of videos, captured from the first person perspective. At the same time, surveillance cameras and drones offer an abundance of visual information, often captured from top-view. Although these two sources of information have been separately studied in the past, they have not been collectively studied and related. Having a set of egocentric cameras and a top-view camera capturing the same area, we propose a framework to identify the egocentric viewers in the top-view video. We utilize two types of features for our assignment procedure. Unary features encode what a viewer (seen from top-view or recording an egocentric video) visually experiences over time. Pairwise features encode the relationship between the visual content of a pair of viewers. Modeling each view (egocentric or top) by a graph, the assignment process is formulated as spectral graph matching. Evaluating our method over a dataset of 50 top-view and 188 egocentric videos taken in different scenarios demonstrates the efficiency of the proposed approach in assigning egocentric viewers to identities present in top-view camera. We also study the effect of different parameters such as the number of egocentric viewers and visual features.
  @cite_33: As tracking systems become more effective at reliably tracking multiple objects over extended periods of time within single camera views and across overlapping camera views, increasing attention is being focused on tracking objects through periods where they are not observed. This paper investigates an unsupervised hypothesis testing method for learning the characteristics of objects passing unobserved from one observed location to another. This method not only reliably determines whether objects predictably pass from one location to another without performing explicit correspondence, but it approximates the likelihood of those transitions. It is robust to non-stationary traffic processes that result from traffic lights, vehicle grouping, and other non-linear vehicle-vehicle interactions. Synthetic data allows us to test and verify our results for complex traffic situations over multiple city blocks and contrast it with previous approaches.
  @cite_28: We present an approach for inferring the topology of a camera network by measuring statistical dependence between observations in different cameras. Two cameras are considered connected if objects seen departing in one camera is seen arriving in the other. This is captured by the degree of statistical dependence between the cameras. The nature of dependence is characterized by the distribution of observation transformations between cameras, such as departure to arrival transition times, and color appearance. We show how to measure statistical dependence when the correspondence between observations in different cameras is unknown. This is accomplished by non-parametric estimates of statistical dependence and Bayesian integration of the unknown correspondence. Our approach generalizes previous work which assumed restricted parametric transition distributions and only implicitly dealt with unknown correspondence. Results are shown on simulated and real data. We also describe a technique for learning the absolute locations of the cameras with Global Positioning System (GPS) side information
  @cite_29: 
  @cite_39: 
  @cite_49: Tracking across cameras with non-overlapping views is a challenging problem. Firstly, the observations of an object are often widely separated in time and space when viewed from non-overlapping cameras. Secondly, the appearance of an object in one camera view might be very different from its appearance in another camera view due to the differences in illumination, pose and camera properties. To deal with the first problem, we observe that people or vehicles tend to follow the same paths in most cases, i.e., roads, walkways, corridors etc. The proposed algorithm uses this conformity in the traversed paths to establish correspondence. The algorithm learns this conformity and hence the inter-camera relationships in the form of multivariate probability density of space-time variables (entry and exit locations, velocities, and transition times) using kernel density estimation. To handle the appearance change of an object as it moves from one camera to another, we show that all brightness transfer functions from a given camera to another camera lie in a low dimensional subspace. This subspace is learned by using probabilistic principal component analysis and used for appearance matching. The proposed approach does not require explicit inter-camera calibration, rather the system learns the camera topology and subspace of inter-camera brightness transfer functions during a training phase. Once the training is complete, correspondences are assigned using the maximum likelihood (ML) estimation framework using both location and appearance cues. Experiments with real world videos are reported which validate the proposed approach.
  @cite_23: This paper is about tracking people in real-time as they move through the non-overlapping fields of view of multiple video cameras The paper builds upon existing methods for tracking moving objects in a single camera The key extension is the use of a stochastic transition matrix to describe people's observed patterns of motion both within and between fields of view The parameters of the model for a particular environment are learnt simply by observing a person moving about in that environment No knowledge of the environment or the configuration of the cameras is required.
  @cite_46: 
  @cite_12: Given two to four synchronized video streams taken at eye level and from different angles, we show that we can effectively combine a generative model with dynamic programming to accurately follow up to six individuals across thousands of frames in spite of significant occlusions and lighting changes. In addition, we also derive metrically accurate trajectories for each of them. Our contribution is twofold. First, we demonstrate that our generative model can effectively handle occlusions in each time frame independently, even when the only data available comes from the output of a simple background subtraction algorithm and when the number of individuals is unknown a priori. Second, we show that multiperson tracking can be reliably achieved by processing individual trajectories separately over long sequences, provided that a reasonable heuristic is used to rank these individuals and that we avoid confusing them with one another.
summary: The spatio-temporal relationships across cameras @cite_39 @cite_33 @cite_28 @cite_62 @cite_29 or prior knowledge about topology has also been used for human re-identification. Chen al @cite_30 make use of prior knowledge about camera topology to adaptively learn appearance and spatio-temporal relationships between cameras, while Mazzon al @cite_46 use prior knowledge about relative locations of cameras to limit potential paths people can follow. Javed al @cite_49 presented a two-phase approach where transition times and exit entrance relationships are learned first, which are later used to improve object correspondences. Fleuret @cite_12 predicted occlusions with a generative model and a probabilistic occupancy map. Dick and Brooks @cite_23 used a stochastic transition matrix to model patterns of motion within and across cameras. These methods have been evaluated on non-crowded scenarios, where observations are sparse and appearance is distinctive. In crowded scenes, hundreds of people enter a camera simultaneously within a small window of few seconds, which makes learning transition times during an unsupervised training period virtually impossible. Furthermore, since it is not always possible to obtain camera topology information, our approach is applicable whether or not the camera topology is available.