Review of Person Re-identification Techniques

Person re-identification across different surveillance cameras with disjoint fields of view has become one of the most interesting and challenging subjects in the area of intelligent video surveillance. Although several methods have been developed and proposed, certain limitations and unresolved issues remain. In all of the existing re-identification approaches, feature vectors are extracted from segmented still images or video frames. Different similarity or dissimilarity measures have been applied to these vectors. Some methods have used simple constant metrics, whereas others have utilised models to obtain optimised metrics. Some have created models based on local colour or texture information, and others have built models based on the gait of people. In general, the main objective of all these approaches is to achieve a higher-accuracy rate and lowercomputational costs. This study summarises several developments in recent literature and discusses the various available methods used in person re-identification. Specifically, their advantages and disadvantages are mentioned and compared.Comment: Published 201

Incorporating spatial relationship information in signal-to-text processing

This dissertation outlines the development of a signal-to-text system that incorporates spatial relationship information to generate scene descriptions. Existing signal-to-text systems generate accurate descriptions in regards to information contained in an image. However, to date, no signalto- text system incorporates spatial relationship information. A survey of related work in the fields of object detection, signal-to-text, and spatial relationships in images is presented first. Three methodologies followed by evaluations were conducted in order to create the signal-to-text system: 1) generation of object localization results from a set of input images, 2) derivation of Level One Summaries from an input image, and 3) inference of Level Two Summaries from the derived Level One Summaries. Validation processes are described for the second and third evaluations, as the first evaluation has been previously validated in the related original works. The goal of this research is to show that a signal-to-text system that incorporates spatial information results in more informative descriptions of the content contained in an image. An additional goal of this research is to demonstrate the signal-to-text system can be easily applied to additional data sets, other than the sets used to train the system, and achieve similar results to the training sets. To achieve this goal, a validation study was conducted and is presented to the reader

K2: A new method for the detection of galaxy clusters based on CFHTLS multicolor images

We have developed a new method, K2, optimized for the detection of galaxy clusters in multicolor images. Based on the Red Sequence approach, K2 detects clusters using simultaneous enhancements in both colors and position. The detection significance is robustly determined through extensive Monte-Carlo simulations and through comparison with available cluster catalogs based on two different optical methods, and also on X-ray data. K2 also provides quantitative estimates of the candidate clusters' richness and photometric redshifts. Initially K2 was applied to 161 sq deg of two color gri images of the CFHTLS-Wide data. Our simulations show that the false detection rate, at our selected threshold, is only ~1%, and that the cluster catalogs are ~80% complete up to a redshift of 0.6 for Fornax-like and richer clusters and to z ~0.3 for poorer clusters. Based on Terapix T05 release gri photometric catalogs, 35 clusters/sq deg are detected, with 1-2 Fornax-like or richer clusters every two square degrees. Catalogs containing data for 6144 galaxy clusters have been prepared, of which 239 are rich clusters. These clusters, especially the latter, are being searched for gravitational lenses -- one of our chief motivations for cluster detection in CFHTLS. The K2 method can be easily extended to use additional color information and thus improve overall cluster detection to higher redshifts. The complete set of K2 cluster catalogs, along with the supplementary catalogs for the member galaxies, are available on request from the authors.Comment: Accepted in ApJ. 25 pages, including 10 figures. Latex with emulateapj v03/07/0

A Fuzzy Logic-Based System for Soccer Video Scenes Classification

Massive global video surveillance worldwide captures data but lacks detailed activity information to flag events of interest, while the human burden of monitoring video footage is untenable. Artificial intelligence (AI) can be applied to raw video footage to identify and extract required information and summarize it in linguistic formats. Video summarization automation usually involves text-based data such as subtitles, segmenting text and semantics, with little attention to video summarization in the processing of video footage only. Classification problems in recorded videos are often very complex and uncertain due to the dynamic nature of the video sequence and light conditions, background, camera angle, occlusions, indistinguishable scene features, etc. Video scene classification forms the basis of linguistic video summarization, an open research problem with major commercial importance. Soccer video scenes present added challenges due to specific objects and events with similar features (e.g. “people” include audiences, coaches, and players), as well as being constituted from a series of quickly changing and dynamic frames with small inter-frame variations. There is an added difficulty associated with the need to have light weight video classification systems working in real time with massive data sizes. In this thesis, we introduce a novel system based on Interval Type-2 Fuzzy Logic Classification Systems (IT2FLCS) whose parameters are optimized by the Big Bang–Big Crunch (BB-BC) algorithm, which allows for the automatic scenes classification using optimized rules in broadcasted soccer matches video. The type-2 fuzzy logic systems would be unequivocal to present a highly interpretable and transparent model which is very suitable for the handling the encountered uncertainties in video footages and converting the accumulated data to linguistic formats which can be easily stored and analysed. Meanwhile the traditional black box techniques, such as support vector machines (SVMs) and neural networks, do not provide models which could be easily analysed and understood by human users. The BB-BC optimization is a heuristic, population-based evolutionary approach which is characterized by the ease of implementation, fast convergence and low computational cost. We employed the BB-BC to optimize our system parameters of fuzzy logic membership functions and fuzzy rules. Using the BB-BC we are able to balance the system transparency (through generating a small rule set) together with increasing the accuracy of scene classification. Thus, the proposed fuzzy-based system allows achieving relatively high classification accuracy with a small number of rules thus increasing the system interpretability and allowing its real-time processing. The type-2 Fuzzy Logic Classification System (T2FLCS) obtained 87.57% prediction accuracy in the scene classification of our testing group data which is better than the type-1 fuzzy classification system and neural networks counterparts. The BB-BC optimization algorithms decrease the size of rule bases both in T1FLCS and T2FLCS; the T2FLCS finally got 85.716% with reduce rules, outperforming the T1FLCS and neural network counterparts, especially in the “out-of-range data” which validates the T2FLCSs capability to handle the high level of faced uncertainties. We also presented a novel approach based on the scenes classification system combined with the dynamic time warping algorithm to implement the video events detection for real world processing. The proposed system could run on recorded or live video clips and output a label to describe the event in order to provide the high level summarization of the videos to the user

Learning and Using Taxonomies For Fast Visual Categorization

The computational complexity of current visual categorization algorithms scales linearly at best with the number of categories. The goal of classifying simultaneously N_(cat) = 10^4 - 10^5 visual categories requires sub-linear classification costs. We explore algorithms for automatically building classification trees which have, in principle, log N_(cat) complexity. We find that a greedy algorithm that recursively splits the set of categories into the two minimally confused subsets achieves 5-20 fold speedups at a small cost in classification performance. Our approach is independent of the specific classification algorithm used. A welcome by-product of our algorithm is a very reasonable taxonomy of the Caltech-256 dataset

Stellar Proper Motions in the Galactic Bulge from deep HST ACS/WFC Photometry

We present stellar proper motions in the Galactic bulge from the Sagittarius Window Eclipsing Extrasolar Search (SWEEPS) project using ACS/WFC on HST. Proper motions are extracted for more than 180,000 objects, with >81,000 measured to accuracy better than 0.3 mas/yr in both coordinates. We report several results based on these measurements: 1. Kinematic separation of bulge from disk allows a sample of >15,000 bulge objects to be extracted based on >6-sigma detections of proper motion, with <0.2% contamination from the disk. This includes the first detection of a candidate bulge Blue Straggler population. 2. Armed with a photometric distance modulus on a star by star basis, and using the large number of stars with high-quality proper motion measurements to overcome intrinsic scatter, we dissect the kinematic properties of the bulge as a function of distance along the line of sight. This allows us to extract the stellar circular speed curve from proper motions alone, which we compare with the circular speed curve obtained from radial velocities. 3. We trace the variation of the {l,b} velocity ellipse as a function of depth. 4. Finally, we use the density-weighted {l,b} proper motion ellipse produced from the tracer stars to assess the kinematic membership of the sixteen transiting planet candidates discovered in the Sagittarius Window; the kinematic distribution of the planet candidates is consistent with that of the disk and bulge stellar populations.Comment: 71 pages, 30 figures, ApJ Accepte

Spectroscopy of new brown dwarf members of rho Ophiuchi and an updated initial mass function

To investigate the universality hypothesis of the initial mass function in the substellar regime, the population of the rho Ophiuchi molecular cloud is analysed by including a new sample of low-mass spectroscopically confirmed members. To that end, we have conducted a large spectroscopic follow-up of young substellar candidates uncovered in our previous photometric survey. The spectral types and extinction were derived for a newly found population of substellar objects, and its masses estimated by comparison to evolutionary models. A thoroughly literature search was conducted to provide an up-to-date census of the cluster, which was then used to derive the luminosity and mass functions, as well as the ratio of brown dwarfs to stars in the cluster. These results were then compared to other young clusters. It is shown that the study of the substellar population of the rho Ophiuchi molecular cloud is hampered only by the high extinction in the cluster ruling out an apparent paucity of brown dwarfs. The discovery of 16 new members of rho Ophiuchi, 13 of them in the substellar regime, reveals the low-mass end of its population and shows the success of our photometric candidate selection with the WIRCam survey. The study of the brown dwarf population of the cluster reveals a high disk fraction of 76 (+5-8)%. Taking the characteristic peak mass of the derived mass function and the ratio of brown dwarfs to stars into account, we conclude that the mass function of rho Ophiuchi is similar to other nearby young clusters.Comment: Accepted to A&A (30 December 2011); v2 includes language editin