Mengran Gou

Person Re-Identification Using Kernel-Based Metric Learning Methods

Re-identification of individuals across camera networks with limited or no overlapping fields of view remains challenging in spite of significant research efforts. In this paper, we propose the use, and extensively evaluate the performance, of four alternatives for re-ID classification: regularized Pairwise Constrained Component Analysis, kernel Local Fisher Discriminant Analysis, Marginal Fisher Analysis and a ranking ensemble voting scheme, used in conjunction with different sizes of sets of histogram-based features and linear, χ 2 and RBF-χ 2 kernels. Comparisons against the state-of-art show significant improvements in performance measured both in terms of Cumulative Match Characteristic curves (CMC) and Proportion of Uncertainty Removed (PUR) scores on the challenging VIPeR, iLIDS, CAVIAR and 3DPeS datasets.

From the Lab to the Real World: Re-identification in an Airport Camera Network

Over the past ten years, human re-identification has received increased attention from the computer vision research community. However, for the most part, these research papers are divorced from the context of how such algorithms would be used in a real-world system. This paper describes the unique opportunity our group of academic researchers had to design and deploy a human re-identification system in a demanding real-world environment: a busy airport. The system had to be designed from the ground up, including robust modules for real-time human detection and tracking, a distributed, low-latency software architecture, and a front-end user interface designed for a specific scenario. None of these issues are typically addressed in re-identification research papers, but all are critical to an effective system that end users would actually be willing to adopt. We detail the challenges of the real-world airport environment, the computer vision algorithms underlying our human detection and re-identification algorithms, our robust software architecture, and the ground-truthing system required to provide the training and validation data for the algorithms. Our initial results show that despite the challenges and constraints of the airport environment, the proposed system achieves very good performance while operating in real time.

Efficient Temporal Sequence Comparison and Classification Using Gram Matrix Embeddings on a Riemannian Manifold

In this paper we propose a new framework to compare and classify temporal sequences. The proposed approach captures the underlying dynamics of the data while avoiding expensive estimation procedures, making it suitable to process large numbers of sequences. The main idea is to first embed the sequences into a Riemannian manifold by using positive definite regularized Gram matrices of their Hankelets. The advantages of the this approach are: 1) it allows for using non-Euclidean similarity functions on the Positive Definite matrix manifold, which capture better the underlying geometry than directly comparing the sequences or their Hankel matrices, and 2) Gram matrices inherit desirable properties from the underlying Hankel matrices: their rank measure the complexity of the underlying dynamics, and the order and coefficients of the associated regressive models are invariant to affine transformations and varying initial conditions. The benefits of this approach are illustrated with extensive experiments in 3D action recognition using 3D joints sequences. In spite of its simplicity, the performance of this approach is competitive or better than using state-of-art approaches for this problem. Further, these results hold across a variety of metrics, supporting the idea that the improvement stems from the embedding itself, rather than from using one of these metrics.

DukeMTMC4ReID: A Large-Scale Multi-camera Person Re-identification Dataset

In the past decade, research in person re-identification (re-id) has exploded due to its broad use in security and surveillance applications. Issues such as inter-camera viewpoint, illumination and pose variations make it an extremely difficult problem. Consequently, many algorithms have been proposed to tackle these issues. To validate the efficacy of re-id algorithms, numerous benchmarking datasets have been constructed. While early datasets contained relatively few identities and images, several large-scale datasets have recently been proposed, motivated by data-driven machine learning. In this paper, we introduce a new large-scale real-world re-id dataset, DukeMTMC4ReID, using 8 disjoint surveillance camera views covering parts of the Duke University campus. The dataset was created from the recently proposed fully annotated multi-target multi-camera tracking dataset DukeMTMC[36]. A benchmark summarizing extensive experiments with many combinations of existing re-id algorithms on this dataset is also provided for an up-to-date performance analysis.

Automated video-mosaicking approach for confocal microscopic imaging in vivo : an approach to address challenges in imaging living tissue and extend field of view

We describe a computer vision-based mosaicking method for in vivo videos of reflectance confocal microscopy (RCM). RCM is a microscopic imaging technique, which enables the users to rapidly examine tissue in vivo. Providing resolution at cellular-level morphology, RCM imaging combined with mosaicking has shown to be highly sensitive and specific for non-invasively guiding skin cancer diagnosis. However, current RCM mosaicking techniques with existing microscopes have been limited to two-dimensional sequences of individual still images, acquired in a highly controlled manner, and along a specific predefined raster path, covering a limited area. The recent advent of smaller handheld microscopes is enabling acquisition of videos, acquired in a relatively uncontrolled manner and along an ad-hoc arbitrarily free-form, non-rastered path. Mosaicking of video-images (video-mosaicking) is necessary to display large areas of tissue. Our video-mosaicking methods addresses this need. The method can handle unique challenges encountered during video capture such as motion blur artifacts due to rapid motion of the microscope over the imaged area, warping in frames due to changes in contact angle and varying resolution with depth. We present test examples of video-mosaics of melanoma and non-melanoma skin cancers, to demonstrate potential clinical utility.

Video-mosaicking of in vivo reflectance confocal microscopy images for noninvasive examination of skin lesion (Conference Presentation)

In this report we describe a computer vision based pipeline to convert in-vivo reflectance confocal microscopy (RCM) videos collected with a handheld system into large field of view (FOV) mosaics. For many applications such as imaging of hard to access lesions, intraoperative assessment of MOHS margins, or delineation of lesion margins beyond clinical borders, raster scan based mosaicing techniques have clinically significant limitations. In such cases, clinicians often capture RCM videos by freely moving a handheld microscope over the area of interest, but the resulting videos lose large-scale spatial relationships. Videomosaicking is a standard computational imaging technique to register, and stitch together consecutive frames of videos into large FOV high resolution mosaics. However, mosaicing RCM videos collected in-vivo has unique challenges: (i) tissue may deform or warp due to physical contact with the microscope objective lens, (ii) discontinuities or “jumps” between consecutive images and motion blur artifacts may occur, due to manual operation of the microscope, and (iii) optical sectioning and resolution may vary between consecutive images due to scattering and aberrations induced by changes in imaging depth and tissue morphology. We addressed these challenges by adapting or developing new algorithmic methods for videomosaicking, specifically by modeling non-rigid deformations, followed by automatically detecting discontinuities (cut locations) and, finally, applying a data-driven image stitching approach that fully preserves resolution and tissue morphologic detail without imposing arbitrary pre-defined boundaries. We will present example mosaics obtained by clinical imaging of both melanoma and non-melanoma skin cancers. The ability to combine freehand mosaicing for handheld microscopes with preserved cellular resolution will have high impact application in diverse clinical settings, including low-resource healthcare systems.