Learning Discriminative Features for Person Re-Identification

For fulfilling the requirements of public safety in modern cities, more and more large-scale surveillance camera systems are deployed, resulting in an enormous amount of visual data. Automatically processing and interpreting these data promote the development and application of visual data analytic technologies. As one of the important research topics in surveillance systems, person re-identification (re-id) aims at retrieving the target person across non-overlapping camera-views that are implemented in a number of distributed space-time locations. It is a fundamental problem for many practical surveillance applications, eg, person search, cross-camera tracking, multi-camera human behavior analysis and prediction, and it received considerable attentions nowadays from both academic and industrial domains. Learning discriminative feature representation is an essential task in person re-id. Although many methodologies have been proposed, discriminative re-id feature extraction is still a challenging problem due to: (1) Intra- and inter-personal variations. The intrinsic properties of the camera deployment in surveillance system lead to various changes in person poses, view-points, illumination conditions etc. This may result in the large intra-personal variations and/or small inter-personal variations, thus incurring problems in matching person images. (2) Domain variations. The domain variations between different datasets give rise to the problem of generalization capability of re-id model. Directly applying a re-id model trained on one dataset to another one usually causes a large performance degradation. (3) Difficulties in data creation and annotation. Existing person re-id methods, especially deep re-id methods, rely mostly on a large set of inter-camera identity labelled training data, requiring a tedious data collection and annotation process. This leads to poor scalability in practical person re-id applications. Corresponding to the challenges in learning discriminative re-id features, this thesis contributes to the re-id domain by proposing three related methodologies and one new re-id setting: (1) Gaussian mixture importance estimation. Handcrafted features are usually not discriminative enough for person re-id because of noisy information, such as background clutters. To precisely evaluate the similarities between person images, the main task of distance metric learning is to filter out the noisy information. Keep It Simple and Straightforward MEtric (KISSME) is an effective method in person re-id. However, it is sensitive to the feature dimensionality and cannot capture the multi-modes in dataset. To this end, a Gaussian Mixture Importance Estimation re-id approach is proposed, which exploits the Gaussian Mixture Models for estimating the observed commonalities of similar and dissimilar person pairs in the feature space. (2) Unsupervised domain-adaptive person re-id based on pedestrian attributes. In person re-id, person identities are usually not overlapped among different domains (or datasets) and this raises the difficulties in generalizing re-id models. Different from person identity, pedestrian attributes, eg., hair length, clothes type and color, are consistent across different domains (or datasets). However, most of re-id datasets lack attribute annotations. On the other hand, in the field of pedestrian attribute recognition, there is a number of datasets labeled with attributes. Exploiting such data for re-id purpose can alleviate the shortage of attribute annotations in re-id domain and improve the generalization capability of re-id model. To this end, an unsupervised domain-adaptive re-id feature learning framework is proposed to make full use of attribute annotations. Specifically, an existing unsupervised domain adaptation method has been extended to transfer attribute-based features from attribute recognition domain to the re-id domain. With the proposed re-id feature learning framework, the domain invariant feature representations can be effectively extracted. (3) Intra-camera supervised person re-id. Annotating the large-scale re-id datasets requires a tedious data collection and annotation process and therefore leads to poor scalability in practical person re-id applications. To overcome this fundamental limitation, a new person re-id setting is considered without inter-camera identity association but only with identity labels independently annotated within each camera-view. This eliminates the most time-consuming and tedious inter-camera identity association annotating process and thus significantly reduces the amount of human efforts required during annotation. It hence gives rise to a more scalable and more feasible learning scenario, which is named as Intra-Camera Supervised (ICS) person re-id. Under this ICS setting, a new re-id method, i.e., Multi-task Mulit-label (MATE) learning method, is formulated. Given no inter-camera association, MATE is specially designed for self-discovering the inter-camera identity correspondence. This is achieved by inter-camera multi-label learning under a joint multi-task inference framework. In addition, MATE can also efficiently learn the discriminative re-id feature representations using the available identity labels within each camera-view

Pulsar discovery prospect of FASTA

The Five-hundred-meter Aperture Spherical radio Telescope (FAST) has discovered more than 650 new pulsars, which account for 20% of our known Galactic pulsar population. In this paper, we estimate the prospect of a pulsar survey with a radio telescope array to be planned -- the FAST Array (FASTA), consists of six "FAST-type" telescopes. Such a sensitive radio telescope array would be a powerful instrument in probing the pulsar population deep into our Galaxy as well as in nearby galaxies. We simulate the FASTA pulsar discovery prospects with different Galactic pulsar population models and instrumental parameter combinations. We find that FASTA could detect tens of thousands of canonical pulsars and well-over thousands of millisecond pulsars. We also estimate the potential yield if the FASTA is used to search for pulsars from the nearby spiral galaxy M31, and find that it would probably discover around a hundred new radio pulsars

Comparison between anterolateral thigh perforator free flaps and pectoralis major pedicled flap for reconstruction in oral cancer patients-A quality of life analysis

The aim of this study was to compare the differences between anterolateral thigh perforator free flaps (ALTFF) and pectoralis major myocutaneous flap (PMMF) for reconstruction in oral cancer patients. Method and Patients: who received free flap or PMMF reconstruction after ablation surgeries were eligible for the current study. The patients' demographic data, medical history, and quality of life scores(Medical Outcomes Study-Short Form-36 (MOS SF-36) and the University of Washington Quality of Life (UW-QOL) questionnaires were collected. Results: 81 of 118 questionnaires were returned (68.64%). There was signi.cant differences between two groups in the gender (P<0.005). Patients reconstructed with ALTFF had better appearance domains and better shoulders domains, in addition to better role emotion domains. Conclusions: Using either PMMF or ALTFF for reconstruction of oral defects after cancer resection signi.cantly in.uences a patient's quality of life. Data from this study provide useful information for physicians and patients during their discussion of reconstruction modalities for oral cancers

Vertical Velocity Distribution in Open-Channel Flow with Rigid Vegetation

In order to experimentally investigate the effects of rigid vegetation on the characteristics of flow, the vegetations were modeled by rigid cylindrical rod. Flow field is measured under the conditions of submerged rigid rod in flume with single layer and double layer vegetations. Experiments were performed for various spacings of the rigid rods. The vegetation models were aligned with the approaching flow in a rectangular channel. Vertical distributions of time-averaged velocity at various streamwise distances were evaluated using an acoustic Doppler velocimeter (ADV). The results indicate that, in submerged conditions, it is difficult to described velocity distribution along the entire depth using unified function. The characteristic of vertical distribution of longitudinal velocity is the presence of inflection. Under the inflection, the line is convex and groove above inflection. The interaction of high and low momentum fluids causes the flow to fold and creates strong vortices within each mixing layer. Understanding the flow phenomena in the area surrounding the tall vegetation, especially in the downstream region, is very important when modeling or studying the riparian environment. ADV measures of rigid vegetation distribution of the flow velocity field can give people a new understanding

Im/possible Lives: Gender, Class, Self-Fashioning, and Affinal Solidarity in Modern South Asia

Drawing on ethnographic research and employing a micro-historical approach that recognizes not only the transnational but also the culturally specific manifestations of modernity, this article centers on the efforts of a young woman to negotiate shifting and conflicting discourses about what a good life might consist of for a highly educated and high caste Hindu woman living at the margins of a nonetheless globalized world. Newly imaginable worlds in contemporary Mithila,South Asia, structure feeling and action in particularly gendered and classed ways, even as the capacity of individuals to actualize those worlds and the “modern” selves envisioned within them are constrained by both overt and subtle means. In the context of shifting cultural anchors, new practices of silence, literacy, and even behaviors interpreted as “mental illness” may become tactics in an individual’s negotiation of conflicting self-representations. The confluence of forces at play in contemporary Mithila, moreover, is creating new structures of feeling that may begin to reverse long-standing locally held assumptions about strong solidarities between natal families and daughters, on the one hand, and weak solidarities between affinal families and new daughters-in-law, on the other

The electrophysiological characteristics of social exclusion: the perspective of close and distant relationships

The sources of social exclusion are very wide, ranging from the closest people to strangers. However, current studies mainly reveal the electrophysiological characteristics of social exclusion by means of binary comparison between social exclusion and social inclusion, and lack of in-depth analysis of the differences caused by different sources of exclusion. In this study, a static passing ball paradigm system including close and distant relationship identity information was used to reveal the electrophysiological characteristics of individuals when they were excluded by people with different close and distant relationships. The results showed that there was a degree effect of P2, P3a, and LPC components when individuals were excluded by people with different close and distant relationships. Specifically, the amplitude of P2, P3a, and LPC components was larger when individuals were excluded by more distant people. The results indicated that individuals would become more alert and perceive stronger exclusion experience when they were excluded by more distant people, which provided more diversified evidence for the conclusion that electrophysiological components were larger under the condition of exclusion, and revealed the electrophysiological basis behind the multiple motivation models. The results also helped to explain the physiological reasons behind individuals’ different coping behaviors toward excluder with different importance of relationship

Coordinated Control of a Wind-Methanol-Fuel Cell System with Hydrogen Storage

This paper presents a wind-methanol-fuel cell system with hydrogen storage. It can manage various energy flow to provide stable wind power supply, produce constant methanol, and reduce CO2 emissions. Firstly, this study establishes the theoretical basis and formulation algorithms. And then, computational experiments are developed with MATLAB/Simulink (R2016a, MathWorks, Natick, MA, USA). Real data are used to fit the developed models in the study. From the test results, the developed system can generate maximum electricity whilst maintaining a stable production of methanol with the aid of a hybrid energy storage system (HESS). A sophisticated control scheme is also developed to coordinate these actions to achieve satisfactory system performance