Towards Person Identification and Re-identification with Attributes

Abstract. Visual identification of an individual in a crowded environ-ment observed by a distributed camera network is critical to a variety of tasks including commercial space management, border control, and crime prevention. Automatic re-identification of a human from public space CCTV video is challenging due to spatiotemporal visual feature varia-tions and strong visual similarity in people’s appearance, compounded by low-resolution and poor quality video data. Relying on re-identification using a probe image is limiting, as a linguistic description of an individ-ual’s profile may often be the only available cues. In this work, we show how mid-level semantic attributes can be used synergistically with low-level features for both identification and re-identification. Specifically, we learn an attribute-centric representation to describe people, and a met-ric for comparing attribute profiles to disambiguate individuals. This differs from existing approaches to re-identification which rely purely on bottom-up statistics of low-level features: it allows improved robustness to view and lighting; and can be used for identification as well as re-identification. Experiments demonstrate the flexibility and effectiveness of our approach compared to existing feature representations when ap-plied to benchmark datasets.

Learning Behavioural Context

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Background subtraction with Dirichlet processes

Abstract. Background subtraction is an important first step for video analysis, where it is used to discover the objects of interest for fur-ther processing. Such an algorithm often consists of a background model and a regularisation scheme. The background model determines a per-pixel measure of if a pixel belongs to the background or the foreground, whilst the regularisation brings in information from adjacent pixels. A new method is presented that uses a Dirichlet process Gaussian mixture model to estimate a per-pixel background distribution, which is followed by probabilistic regularisation. Key advantages include inferring the per-pixel mode count, such that it accurately models dynamic backgrounds, and that it updates its model continuously in a principled way.

Optical probing technique for inhomogeneous superconducting films

We report a nondestructive optical probing technique for superconducting films, by which a cross-sectional gradient of the local transition temperature Tc and a two-dimensional map of the local critical current I c of an Al film were obtained. The two-dimensional map clearly shows a variety of defects of the Al film. Some of them can be correlated to visible pinholes

TRANSIENT RESPONSE OF SUPERCONDUCTING Pb MICROBRIDGES IRRADIATED BY PICOSECOND LASER PULSES AND ITS POTENTIAL APPLICATIONS

The authors observed voltage pulses having half-widths of less than 500 ps generated by constant-current-biased superconducting Pb variable thickness microbridges driven normal by short (3 - 5 ps) light pulses. This represents a step to generate even shorter pulses, which according to an analysis of the Rothwarf-Taylor equations should be possible. The ultimate width should be equal to the phonon pair-breaking time which, for materials such as Nb, can be a few picoseconds. In addition to monitoring the voltage pulses directly use was made of a novel adoption of the optical autocorrelation technique having a time resolution limited only by the laser pulse width. It is pointed out that even shorter voltage pulses, and therefore greater potential for device applications, can be achieved by direct injection of quasiparticles

A survey on heterogeneous face recognition: Sketch, infra-red, 3D and low-resolution

Heterogeneous face recognition (HFR) refers to matching face imagery across different domains. It has received much interest from the research community as a result of its profound implications in law enforcement. A wide variety of new invariant features, cross-modality matching models and heterogeneous datasets are being established in recent years. This survey provides a comprehensive review of established techniques and recent developments in HFR. Moreover, we offer a detailed account of datasets and benchmarks commonly used for evaluation. We finish by assessing the state of the field and discussing promising directions for future research

Person reidentification: What features are important

Abstract. State-of-the-art person re-identification methods seek robust person matching through combining various feature types. Often, these features are implicitly assigned with a single vector of global weights, which are assumed to be universally good for all individuals, independent to their different appearances. In this study, we show that certain features play more important role than others under different circumstances. Consequently, we propose a novel unsupervised approach for learning a bottom-up feature importance, so features extracted from different individuals are weighted adaptively driven by their unique and inherent appearance attributes. Extensive experiments on two public datasets demonstrate that attribute-sensitive feature importance facilitates more accurate person matching when it is fused together with global weights obtained using existing methods.

F-center-laser. Superconductivity

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