Multitarget Tracking in Nonoverlapping Cameras Using a Reference Set

Tracking multiple targets in nonoverlapping cameras are challenging since the observations of the same targets are often separated by time and space. There might be significant appearance change of a target across camera views caused by variations in illumination conditions, poses, and camera imaging characteristics. Consequently, the same target may appear very different in two cameras. Therefore, associating tracks in different camera views directly based on their appearance similarity is difficult and prone to error. In most previous methods, the appearance similarity is computed either using color histograms or based on pretrained brightness transfer function that maps color between cameras. In this paper, a novel reference set based appearance model is proposed to improve multitarget tracking in a network of nonoverlapping cameras. Contrary to previous work, a reference set is constructed for a pair of cameras, containing subjects appearing in both camera views. For track association, instead of directly comparing the appearance of two targets in different camera views, they are compared indirectly via the reference set. Besides global color histograms, texture and shape features are extracted at different locations of a target, and AdaBoost is used to learn the discriminative power of each feature. The effectiveness of the proposed method over the state of the art on two challenging real-world multicamera video data sets is demonstrated by thorough experiments

The future design direction of smart clothing development

Literature indicates that Smart Clothing applications, the next generation of clothing and electronic products, have been struggling to enter the mass market because the consumers’ latent needs have not been recognised. Moreover, the design direction of Smart Clothes remains unclear and unfocused. Nevertheless, a clear design direction is necessary for all product development. Therefore, this research aims to identify the design directions of the emerging Smart Clothes industry by conducting a questionnaire survey and focus groups with its major design contributors. The results reveal that the current strategy of embedding a wide range of electronic functions in a garment is not suitable. This is primarily because it does not match the users’ requirements, purchasing criteria and lifestyle. The results highlight the respondents’ preference for personal healthcare and sportswear applications that suit their lifestyle, are aesthetically attractive, and provide a practical function

The Properties of H{\alpha} Emission-Line Galaxies at z = 2.24

Using deep narrow-band $H_2S1$ and $K_{s}$-band imaging data obtained with CFHT/WIRCam, we identify a sample of 56 H$\alpha$ emission-line galaxies (ELGs) at $z=2.24$ with the 5$\sigma$ depths of $H_2S1=22.8$ and $K_{s}=24.8$ (AB) over 383 arcmin$^{2}$ area in the ECDFS. A detailed analysis is carried out with existing multi-wavelength data in this field. Three of the 56 H$\alpha$ ELGs are detected in Chandra 4 Ms X-ray observation and two of them are classified as AGNs. The rest-frame UV and optical morphologies revealed by HST/ACS and WFC3 deep images show that nearly half of the H$\alpha$ ELGs are either merging systems or with a close companion, indicating that the merging/interacting processes play a key role in regulating star formation at cosmic epoch z=2-3; About 14% are too faint to be resolved in the rest-frame UV morphology due to high dust extinction. We estimate dust extinction from SEDs. We find that dust extinction is generally correlated with H$\alpha$ luminosity and stellar mass (SM). Our results suggest that H$\alpha$ ELGs are representative of star-forming galaxies (SFGs). Applying extinction correction for individual objects, we examine the intrinsic H$\alpha$ luminosity function (LF) at $z=2.24$, obtaining a best-fit Schechter function characterized by a faint-end slope of $\alpha=-1.3$. This is shallower than the typical slope of $\alpha \sim -1.6$ in previous works based on constant extinction correction. We demonstrate that this difference is mainly due to the different extinction corrections. The proper extinction correction is thus key to recovering the intrinsic LF as the extinction globally increases with H$\alpha$ luminosity. Moreover, we find that our H$\alpha$ LF mirrors the SM function of SFGs at the same cosmic epoch. This finding indeed reflects the tight correlation between SFR and SM for the SFGs, i.e., the so-called main sequence.Comment: 15 pages, 12 figures, 2 tables, Received 2013 October 11; accepted 2014 February 13; published 2014 March 18 by Ap

Parsec-scale jet properties of the gamma-ray quasar 3C 286

The quasar 3C~286 is one of two compact steep spectrum sources detected by the {\it Fermi}/LAT. Here, we investigate the radio properties of the parsec(pc)-scale jet and its (possible) association with the $\gamma$-ray emission in 3C~286. The Very Long Baseline Interferometry (VLBI) images at various frequencies reveal a one-sided core--jet structure extending to the southwest at a projected distance of $\sim$1 kpc. The component at the jet base showing an inverted spectrum is identified as the core, with a mean brightness temperature of $2.8\times 10^{9}$~K. The jet bends at about 600 pc (in projection) away from the core, from a position angle of $-135^\circ$ to $-115^\circ$. Based on the available VLBI data, we inferred the proper motion speed of the inner jet as $0.013 \pm 0.011$ mas yr$^{-1}$ ($\beta_{\rm app} = 0.6 \pm 0.5$), corresponding to a jet speed of about $0.5\,c$ at an inclination angle of $48^\circ$ between the jet and the line of sight of the observer. The brightness temperature, jet speed and Lorentz factor are much lower than those of $\gamma$-ray-emitting blazars, implying that the pc-scale jet in 3C~286 is mildly relativistic. Unlike blazars in which $\gamma$-ray emission is in general thought to originate from the beamed innermost jet, the location and mechanism of $\gamma$-ray emission in 3C~286 may be different as indicated by the current radio data. Multi-band spectrum fitting may offer a complementary diagnostic clue of the $\gamma$-ray production mechanism in this source.Comment: 9 pages, 4 figures, accept for publication in MNRA

Project portfolio resource risk assessment considering project interdependency by the fuzzy Bayesian network

Resource risk caused by specific resource sharing or competition among projects due to resource constraints is a major issue in project portfolio management, which challenges the application of risk analysis methods effectively. This paper presents a methodology by using a fuzzy Bayesian network to assess the project portfolio resource risk, determine critical resource risk factors, and propose risk-reduction strategies. In this method, the project portfolio resource risk factors are first identified by taking project interdependency into consideration, and then the Bayesian network model is developed to analyze the risk level of the identified risk factors in which expert judgments and fuzzy set theory are integrated to determine the probabilities of all risk factors to deal with incomplete risk data and information. To reduce the subjectivity of expert judgments, the expert weights are determined by combining experts’ background and reliability degree of expert judgments. A numerical analysis is used to demonstrate the application of the proposed methodology. The results show that project portfolio resource risks can be analyzed effectively and efficiently. Furthermore, “poor communication and cooperation among projects,” “capital difficulty,” and “lack of sharing technology among projects” are considered the leading factors of the project portfolio resource risk. Risk-reduction strategic decisions based on the results of risk assessment can be made, which provide project managers with a useful method or tool to manage project risks