Human object annotation for surveillance video forensics

A system that can automatically annotate surveillance video in a manner useful for locating a person with a given description of clothing is presented. Each human is annotated based on two appearance features: primary colors of clothes and the presence of text/logos on clothes. The annotation occurs after a robust foreground extraction stage employing a modified Gaussian mixture model-based approach. The proposed pipeline consists of a preprocessing stage where color appearance of an image is improved using a color constancy algorithm. In order to annotate color information for human clothes, we use the color histogram feature in HSV space and find local maxima to extract dominant colors for different parts of a segmented human object. To detect text/logos on clothes, we begin with the extraction of connected components of enhanced horizontal, vertical, and diagonal edges in the frames. These candidate regions are classified as text or nontext on the basis of their local energy-based shape histogram features. Further, to detect humans, a novel technique has been proposed that uses contourlet transform-based local binary pattern (CLBP) features. In the proposed method, we extract the uniform direction invariant LBP feature descriptor for contourlet transformed high-pass subimages from vertical and diagonal directional bands. In the final stage, extracted CLBP descriptors are classified by a trained support vector machine. Experimental results illustrate the superiority of our method on large-scale surveillance video data

TRACK VIDEO ANALYSIS AS PHYSICS MEDIA RESEARCH DURING 2016 TO 2020 A BIBLIOMETRIC ANALYSIS

The purpose of this research is (1) To identify the distribution of articles related to the use of TVA as a medium in publications over the past 5 years; (2) To identify the distribution of languages used in the writing of TVA publications; (3) To identify the number of TVA scientific publications as Physics media research; (4) To identify the correlation between subjects used in TVA. The method used in this research is bibliometric analysis that can help researchers in researching the contents of bibliographies, analyzing the correlation of objects from Scopus metadata in the last 5 years, namely 2016 - 2020. From the search results on Scopus with the keyword TVA obtained as many as 24,495 documents. There are three dominant groups, namely Study, State, and Object. TVA deals with studies, research models and learning, related to tracking an object or benchmark, related to community activities or activities in a country.Tujuan dari penelitian ini adalah (1) Mengetahui distribusi artikel yang berkaitan dengan penggunaan TVA sebagai media pada publikasi selama 5 tahun terakhir; (2) Mengetahui distribusi bahasa yang digunakan dalam penulisan publikasi TVA; (3) Mengetahui jumlah publikasi ilmiah TVA sebagai Physics media research; (4) Mengetahui korelasi antara subjek yang digunakan dalam penelitian TVA. Metode yang digunakan dalam penelitian ini adalah analisis bibliometrik yang dapat membantu para peneliti dalam mempelajari isi bibliografi, menganalisis korelasi objek dari metadata Scopus dalam 5 tahun terakhir, yaitu 2016 – 2020. Dari hasil penelusuran di Scopus dengan kata kunci TVA diperoleh sebanyak 24.495 dokumen. Ada tiga kelompok dominan, yaitu Study, State, dan object. TVA berkaitan dengan studi, model penelitian dan pembelajaran Fisika, terkait pelacakan suatu objek atau patokan, terkait dengan kegiatan atau kegiatan masyarakat di suatu negara melalui prinsip Fisika pada TVA

Evaluating feature extractors and dimension reduction methods for near infrared face recognition systems

This study evaluates the performance of global and local feature extractors as well as dimension reduction methods in NIR domain. Zernike moments (ZMs), Independent Component Analysis (ICA), Radon Transform + Discrete Cosine Transform (RDCT), Radon Transform + Discrete Wavelet Transform (RDWT) are employed as global feature extractors and Local Binary Pattern (LBP), Gabor Wavelets (GW), Discrete Wavelet Transform (DWT) and Undecimated Discrete Wavelet Transform (UDWT) are used as local feature extractors. For evaluation of dimension reduction methods Principal Component Analysis (PCA), Kernel Principal Component Analysis (KPCA), Linear Discriminant Analysis + Principal Component Analysis (Fisherface), Kernel Fisher Discriminant Analysis (KFD) and Spectral Regression Discriminant Analysis (SRDA) are used. Experiments conducted on CASIA NIR database and PolyU-NIRFD database indicate that ZMs as a global feature extractor, UDWT as a local feature extractor and SRDA as a dimension reduction method have superior overall performance compared to some other methods in the presence of facial expressions, eyeglasses, head rotation, image noise and misalignments

Video content analysis for automated detection and tracking of humans in CCTV surveillance applications

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The problems of achieving high detection rate with low false alarm rate for human detection and tracking in video sequence, performance scalability, and improving response time are addressed in this thesis. The underlying causes are the effect of scene complexity, human-to-human interactions, scale changes, and scene background-human interactions. A two-stage processing solution, namely, human detection, and human tracking with two novel pattern classifiers is presented. Scale independent human detection is achieved by processing in the wavelet domain using square wavelet features. These features used to characterise human silhouettes at different scales are similar to rectangular features used in [Viola 2001]. At the detection stage two detectors are combined to improve detection rate. The first detector is based on shape-outline of humans extracted from the scene using a reduced complexity outline extraction algorithm. A Shape mismatch measure is used to differentiate between the human and the background class. The second detector uses rectangular features as primitives for silhouette description in the wavelet domain. The marginal distribution of features collocated at a particular position on a candidate human (a patch of the image) is used to describe statistically the silhouette. Two similarity measures are computed between a candidate human and the model histograms of human and non human classes. The similarity measure is used to discriminate between the human and the non human class. At the tracking stage, a tracker based on joint probabilistic data association filter (JPDAF) for data association, and motion correspondence is presented. Track clustering is used to reduce hypothesis enumeration complexity. Towards improving response time with increase in frame dimension, scene complexity, and number of channels; a scalable algorithmic architecture and operating accuracy prediction technique is presented. A scheduling strategy for improving the response time and throughput by parallel processing is also presented