Dr. Iffat Zafar Profile

Dr. Iffat Zafar

at Longborough Univ

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Author

Publications (5)

SPIE Journal Paper | 29 August 2013

Human object annotation for surveillance video forensics

Muhammad Fraz, Iffat Zafar, Giounona Tzanidou, Eran Edirisinghe, Muhammad Saquib Sarfraz

JEI, Vol. 22, Issue 04, 041115, (August 2013) https://doi.org/10.1117/12.10.1117/1.JEI.22.4.041115

KEYWORDS: Video, Video surveillance, Surveillance, Forensic science, Image segmentation, RGB color model, Binary data, Cameras, Video processing, Image processing

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Proceedings Article | 19 March 2013 Paper

Human object articulation for CCTV video forensics

I. Zafar, M. Fraz, Eran Edirisinghe

Proceedings Volume 8663, 86630Z (2013) https://doi.org/10.1117/12.2004956

KEYWORDS: Video, Video surveillance, RGB color model, Feature extraction, Surveillance, Data modeling, Forensic science, Cameras, Head, Image segmentation

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In this paper we present a system which is focused on developing algorithms for automatic annotation/articulation of humans passing through a surveillance camera in a way useful for describing a person/criminal by a crime scene witness. Each human is articulated/annotated based on two appearance features: 1. primary colors of clothes in the head, body and legs region. 2. presence of text/logo on the clothes. The annotation occurs after a robust foreground extraction based on a modified approach to Gaussian Mixture model and detection of human from segmented foreground images. The proposed pipeline consists of a preprocessing stage where we improve color quality of images using a basic color constancy algorithm and further improve the results using a proposed post-processing method. The results show a significant improvement to the illumination of the video frames. In order to annotate color information for human clothes, we apply 3D Histogram analysis (with respect to Hue, Saturation and Value) on HSV converted image regions of human body parts along with extrema detection and thresholding to decide the dominant color of the region. In order to detect text/logo on the clothes as another feature to articulate humans, 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 non-text on the bases of their Local Energy based Shape Histogram (LESH) features combined with KL divergence as classification criteria. To detect humans, a novel technique has been proposed that uses a combination of Histogram of Oriented Gradients (HOG) and Contourlet transform based Local Binary Patterns (LBP) with Adaboost as classifier. Initial screening of foreground objects is performed by using HOG features. To further eliminate the false positives due to noise form background and improve results, we apply Contourlet-LBP feature extraction on the images. In the proposed method, we extract the LBP feature descriptor for Contourlet transformed high pass sub-images from vertical and diagonal directional bands. In the final stage, extracted Contourlet-LBP descriptors are applied to Adaboost for classification. The proposed frame work showed fairly fine performance when tested on a CCTV test dataset.

Proceedings Article | 5 May 2010 Paper

Real-time speaker identification for video conferencing

S. Saravi, I. Zafar, E. Edirisinghe, R. Kalawsky

Proceedings Volume 7724, 77240D (2010) https://doi.org/10.1117/12.854846

KEYWORDS: Laser induced plasma spectroscopy, Video, Facial recognition systems, Detection and tracking algorithms, Edge detection, Sensors, Image processing algorithms and systems, Image registration, Information visualization, Mouth

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Proceedings Article | 5 May 2010 Paper

Real-time multi-barcode reader for industrial applications

Iffat Zafar, Usman Zakir, Eran Edirisinghe

Proceedings Volume 7724, 772404 (2010) https://doi.org/10.1117/12.854645

KEYWORDS: Bandpass filters, Cameras, Optical filters, LED backlight, Image filtering, Light sources and illumination, Image processing, Image segmentation, Linear filtering, Light sources

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Proceedings Article | 3 February 2009 Paper

Localized contourlet features in vehicle make and model recognition

I. Zafar, E. Edirisinghe, B. Acar

Proceedings Volume 7251, 725105 (2009) https://doi.org/10.1117/12.805878

KEYWORDS: Transform theory, Detection and tracking algorithms, Feature extraction, Systems modeling, Image filtering, Feature selection, Analytical research, Performance modeling, Computing systems, Wavelets

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