Junping Zhang

Data-Driven Intelligent Transportation Systems: A Survey

For the last two decades, intelligent transportation systems (ITS) have emerged as an efficient way of improving the performance of transportation systems, enhancing travel security, and providing more choices to travelers. A significant change in ITS in recent years is that much more data are collected from a variety of sources and can be processed into various forms for different stakeholders. The availability of a large amount of data can potentially lead to a revolution in ITS development, changing an ITS from a conventional technology-driven system into a more powerful multifunctional data-driven intelligent transportation system (D2ITS) : a system that is vision, multisource, and learning algorithm driven to optimize its performance. Furthermore, D2ITS is trending to become a privacy-aware people-centric more intelligent system. In this paper, we provide a survey on the development of D2ITS, discussing the functionality of its key components and some deployment issues associated with D2ITS Future research directions for the development of D2ITS is also presented.

Hallucinating face by position-patch

A novel face hallucination method is proposed in this paper for the reconstruction of a high-resolution face image from a low-resolution observation based on a set of high- and low-resolution training image pairs. Different from most of the established methods based on probabilistic or manifold learning models, the proposed method hallucinates the high-resolution image patch using the same position image patches of each training image. The optimal weights of the training image position-patches are estimated and the hallucinated patches are reconstructed using the same weights. The final high-resolution facial image is formed by integrating the hallucinated patches. The necessity of two-step framework or residue compensation and the differences between hallucination based on patch and global image are discussed. Experiments show that the proposed method without residue compensation generates higher-quality images and costs less computational time than some recent face image super-resolution (hallucination) techniques.

Visual Traffic Jam Analysis Based on Trajectory Data

In this work, we present an interactive system for visual analysis of urban traffic congestion based on GPS trajectories. For these trajectories we develop strategies to extract and derive traffic jam information. After cleaning the trajectories, they are matched to a road network. Subsequently, traffic speed on each road segment is computed and traffic jam events are automatically detected. Spatially and temporally related events are concatenated in, so-called, traffic jam propagation graphs. These graphs form a high-level description of a traffic jam and its propagation in time and space. Our system provides multiple views for visually exploring and analyzing the traffic condition of a large city as a whole, on the level of propagation graphs, and on road segment level. Case studies with 24 days of taxi GPS trajectories collected in Beijing demonstrate the effectiveness of our system.

Super-resolution of human face image using canonical correlation analysis

Super-resolution reconstruction of face image is the problem of reconstructing a high resolution face image from one or more low resolution face images. Assuming that high and low resolution images share similar intrinsic geometries, various recent super-resolution methods reconstruct high resolution images based on a weights determined from nearest neighbors in the local embedding of low resolution images. These methods suffer disadvantages from the finite number of samples and the nature of manifold learning techniques, and hence yield unrealistic reconstructed images. To address the problem, we apply canonical correlation analysis (CCA), which maximizes the correlation between the local neighbor relationships of high and low resolution images. We use it separately for reconstruction of global face appearance, and facial details. Experiments using a collection of frontal human faces show that the proposed algorithm improves reconstruction quality over existing state-of-the-art super-resolution algorithms, both visually, and using a quantitative peak signal-to-noise ratio assessment.

Human Identification Using Temporal Information Preserving Gait Template

Gait Energy Image (GEI) is an efficient template for human identification by gait. However, such a template loses temporal information in a gait sequence, which is critical to the performance of gait recognition. To address this issue, we develop a novel temporal template, named Chrono-Gait Image (CGI), in this paper. The proposed CGI template first extracts the contour in each gait frame, followed by encoding each of the gait contour images in the same gait sequence with a multichannel mapping function and compositing them to a single CGI. To make the templates robust to a complex surrounding environment, we also propose CGI-based real and synthetic temporal information preserving templates by using different gait periods and contour distortion techniques. Extensive experiments on three benchmark gait databases indicate that, compared with the recently published gait recognition approaches, our CGI-based temporal information preserving approach achieves competitive performance in gait recognition with robustness and efficiency.

Neighbor embedding based super-resolution algorithm through edge detection and feature selection

Assuming that the local geometry of low-resolution image patches is similar to that of the high-resolution counterparts, neighbor embedding based super-resolution methods learn a high-resolution image from one or more low-resolution input images by embedding its patches optimally with training ones. However, their performance suffers from inappropriate choices of features, neighborhood sizes and training patches. To address the issues, we propose an extended Neighbor embedding based super-resolution through edge detection and Feature Selection (henceforth NeedFS). Three major contributions of NeedFS are: (1) A new combination of features are proposed, which preserve edges and smoothen color regions better; (2) the training patches are learned discriminately with different neighborhood sizes based on edge detection; (3) only those edge training patches are bootstrapped to provide extra useful information with least redundancy. Experiments show that NeedFS performs better in both quantitative and qualitative evaluation. NeedFS is also robust even with a very limited training set and thus is promising for real applications.

Manifold Learning and Applications in Recognition

Great amount of data under varying intrinsic features are empirically thought of as high-dimensional nonlinear manifold in the observation space. With respect to different categories, we present two recognition approaches, i.e. the combination of manifold learning algorithm and linear discriminant analysis (MLA+LDA), and nonlinear auto-associative modeling (NAM). For similar object recognition, e.g. face recognition, MLA + LDA is used. Otherwise, NAM is employed for objects from largely different categories. Experimental results on different benchmark databases show the advantages of the proposed approaches.

Low-Resolution Gait Recognition

Unlike other biometric authentication methods, gait recognition is noninvasive and effective from a distance. However, the performance of gait recognition will suffer in the low-resolution (LR) case. Furthermore, when gait sequences are projected onto a nonoptimal low-dimensional subspace to reduce the data complexity, the performance of gait recognition will also decline. To deal with these two issues, we propose a new algorithm called superresolution with manifold sampling and backprojection (SRMS), which learns the high-resolution (HR) counterparts of LR test images from a collection of HR/LR training gait image patch pairs. Then, we incorporate SRMS into a new algorithm called multilinear tensor-based learning without tuning parameters (MTP) for LR gait recognition. Our contributions include the following: 1) With manifold sampling, the redundancy of gait image patches is remarkably decreased; thus, the superresolution procedure is more efficient and reasonable. 2) Backprojection guarantees that the learned HR gait images and the corresponding LR gait images can be more consistent. 3) The optimal subspace dimension for dimension reduction is automatically determined without introducing extra parameters. 4) Theoretical analysis of the algorithm shows that MTP converges. Experiments on the USF human gait database and the CASIA gait database show the increased efficiency of the proposed algorithm, compared with previous algorithms.

Chrono-gait image: a novel temporal template for gait recognition

In this paper, we propose a novel temporal template, called Chrono-Gait Image (CGI), to describe the spatio-temporal walking pattern for human identification by gait. The CGI temporal template encodes the temporal information among gait frames via color mapping to improve the recognition performance. Our method starts with the extraction of the contour in each gait image, followed by utilizing a color mapping function to encode each of gait contour images in the same gait sequence and compositing them to a single CGI. We also obtain the CGI-based real templates by generating CGI for each period of one gait sequence and utilize contour distortion to generate the CGI-based synthetic templates. In addition to independent recognition using either of individual templates, we combine the real and synthetic temporal templates for refining the performance of human recognition. Extensive experiments on the USF HumanID database indicate that compared with the recently published gait recognition approaches, our CGI-based approach attains better performance in gait recognition with considerable robustness to gait period detection.

Nearest manifold approach for face recognition

Faces under varying illumination, pose and non-rigid deformation are empirically thought of as a highly nonlinear manifold in the observation space. How to discover intrinsic low-dimensional manifold is important to characterize meaningful face distributions and classify them using a simpler, such as linear or Gaussian based, classifier. In this paper, we present a manifold learning algorithm (MLA) for learning a mapping from highly-dimensional manifold into the intrinsic low-dimensional linear manifold. We also propose the nearest manifold (NM) criterion for the classification and present an algorithm for computing the distance from the sample to be classified to the nearest face manifolds in light of local linearity of manifold. Based on these works, face recognition is achieved with the combination of MLA and NM. Experiments on several face databases show that the advantages of our proposed combinational approach.