A review of content-based video retrieval techniques for person identification

The rise of technology spurs the advancement in the surveillance field. Many commercial spaces reduced the patrol guard in favor of Closed-Circuit Television (CCTV) installation and even some countries already used surveillance drone which has greater mobility. In recent years, the CCTV Footage have also been used for crime investigation by law enforcement such as in Boston Bombing 2013 incident. However, this led us into producing huge unmanageable footage collection, the common issue of Big Data era. While there is more information to identify a potential suspect, the massive size of data needed to go over manually is a very laborious task. Therefore, some researchers proposed using Content-Based Video Retrieval (CBVR) method to enable to query a specific feature of an object or a human. Due to the limitations like visibility and quality of video footage, only certain features are selected for recognition based on Chicago Police Department guidelines. This paper presents the comprehensive reviews on CBVR techniques used for clothing, gender and ethnic recognition of the person of interest and how can it be applied in crime investigation. From the findings, the three recognition types can be combined to create a Content-Based Video Retrieval system for person identification

Robust human detection with occlusion handling by fusion of thermal and depth images from mobile robot

In this paper, a robust surveillance system to enable robots to detect humans in indoor environments is proposed. The proposed method is based on fusing information from thermal and depth images which allows the detection of human even under occlusion. The proposed method consists of three stages, pre-processing, ROI generation and object classification. A new dataset was developed to evaluate the performance of the proposed method. The experimental results show that the proposed method is able to detect multiple humans under occlusions and illumination variations

FPGA-based real-time moving target detection system for unmanned aerial vehicle application

Moving target detection is the most common task for Unmanned Aerial Vehicle (UAV) to find and track object of interest from a bird's eye view in mobile aerial surveillance for civilian applications such as search and rescue operation. The complex detection algorithm can be implemented in a real-time embedded system using Field Programmable Gate Array (FPGA). This paper presents the development of real-time moving target detection System-on-Chip (SoC) using FPGA for deployment on a UAV. The detection algorithm utilizes area-based image registration technique which includes motion estimation and object segmentation processes. The moving target detection system has been prototyped on a low-cost Terasic DE2-115 board mounted with TRDB-D5M camera. The system consists of Nios II processor and stream-oriented dedicated hardware accelerators running at 100 MHz clock rate, achieving 30-frame per second processing speed for 640 × 480 pixels' resolution greyscale videos

An improved approach for medical image fusion using sparse representation and Siamese convolutional neural network

Multimodal image fusion is a contemporary branch of medical imaging that aims to increase the accuracy of clinical diagnosis of the disease stage development. The fusion of different image modalities can be a viable medical imaging approach. It combines the best features to produce a composite image with higher quality than its predecessors and can significantly improve medical diagnosis. Recently, sparse representation (SR) and Siamese Convolutional Neural Network (SCNN) methods have been introduced independently for image fusion. However, some of the results from these approaches have recorded defects, such as edge blur, less visibility, and blocking artifacts. To remedy these deficiencies, in this paper, a smart blending approach based on a combination of SR and SCNN is introduced for image fusion, which comprises three steps as follows. Firstly, entire source images are fed into the classical orthogonal matching pursuit (OMP), where the SR-fused image is obtained using the max-rule that aims to improve pixel localization. Secondly, a novel scheme of SCNN-based K-SVD dictionary learning is re-employed for each source image. The method has shown good non-linearity behavior, contributing to increasing the fused output's sparsity characteristics and demonstrating better extraction and transfer of image details to the output fused image. Lastly, the fusion rule step employs a linear combination between steps 1 and 2 to obtain the final fused image. The results depict that the proposed method is advantageous, compared to other previous methods, notably by suppressing the artifacts produced by the traditional SR and SCNN model

Prediction in ungauged river basin in the west coast of peninsular Malaysia using linear regression model

A linear multiple regression based regionalization method has been proposed in this study to simulate streamflow in ungauged catchment in the east coast of peninsular Malaysia. Identification of unit Hydrographs And Component flows from Rainfall, Evapotranspiration and Streamflow (IHACRES) rainfall-runoff model was used to develop the relationship between model parameters and physical catchment descriptors of eight gauged catchments distributed over the west coast of peninsular Malaysia. The IHACRES model was calibrated and validated individually for each catchment with the available data for the time periods varying between three to sixteen years. The Nash-Sutcliffe efficiency index was used as criteria to evaluate the model performance. As the relationships between the physical catchment descriptors and hydrologic response characteristics are not necessarily linear, different forms of transformations were performed in order to find the most appropriate relationship. Finally, the obtained regression equations were used for simulating stream discharge in Sg Layang catchment located in the south of peninsular Malaysia. The result of the regional model was compared with observed monthly stream flow data of the catchment to assess the ability of regional model. The obtained results revealed that the regional model was able to replicate the historical monthly average flow. However, the relationship between the catchment area and hydrologic response characteristics were not fully understood by regional model which emphasize the need of consideration of other dominant catchment factors for prediction in ungauged basin

A comprehensive review of vehicle detection using computer vision

A crucial step in designing intelligent transport systems (ITS) is vehicle detection. The challenges of vehicle detection in urban roads arise because of camera position, background variations, occlusion, multiple foreground objects as well as vehicle pose. The current study provides a synopsis of state-of-the-art vehicle detection techniques, which are categorized according to motion and appearance-based techniques starting with frame differencing and background subtraction until feature extraction, a more complicated model in comparison. The advantages and disadvantages among the techniques are also highlighted with a conclusion as to the most accurate one for vehicle detection

Comparative Study on the Measurement of Human Thermal Activity

Human physiological signals measurement is the necessity of today’s modern world. The physiological signals, including heart rate, skin conductance, temperature, and pupil diameter, are significant indicators of underlying problems or illnesses and aid in indicating the underlying condition non-invasively. This study highlights the importance and needs for only one physiological signal, which is the body temperature as even a minor change in temperature values has a unique effect on the body. Hence, the present study focuses on comparing two well-known temperature sensors, namely DS18B20 and LM35, which are among the top choices for many temperature-based applications. The two sensors are compared in terms of cost, accuracy, temperature range, voltage, output type, implementation, packaging and required signal conditioning circuitry. The sole purpose is to find the adequacy of only one in terms of medical applications. The temperature readings are collected for 15 seconds from 10 participants between the age of 25 – 28 years and the data is sent to a microcontroller, which is Arduino Mega board. The microcontroller board processes the data for noise and artefacts removal and displays the final temperature readings on the serial monitor of Arduino IDE. The results highlight that DS18B20 is more accurate and robust in comparison to LM35, as it has lower fluctuations in the readings and is not affected by user movements. This study will help in the future development of healthcare systems, which may track the user’s thermal changes accurately in real-time

Field measurement of fishing boats generated waves

Ship generated waves can cause banks erosion as well as disturbance on stationary boats. Studies have shown that the boat generated waves are dependent on and affected by environmental factors and vessel parameters. The main environmental factors are tidal and current direction, and for vessel parameters are speeds and hullforms. This paper describes a full-scale experimental work to measure wave heights and wave angle direction on boat generated waves. The measurement method used in this paper is based on the analysis of digital video recordings and image processing techniques

Low-Power And High Performance Of An Optimized FinFET Based 8T SRAM Cell Design

The development of the nanotechnology leadsto the shrinking of the size of the transistors to nanometerregion. However, there are a lot of challenges due to sizescaling of the transistors such as short channel effects (SCEs)and threshold voltage roll-off issues. Fin-Type Field EffectTransistor (FinFET) is another alternative technology tosolve the issues of the conventional MOSFET and increasethe performance of the Static Random Access Memory(SRAM) circuit design. FinFET based SRAMs are faster andmore reliable which are often used as memory cache for highspeed operation. However, 6T SRAM cell suffers from accesstransistor sizing conflict resulting in a trade-off between readand write stability. This paper presents an investigation ofthe stability performance in retention, read and write modeof 22nm FinFET based 8T SRAM cell. The performancecomparison of 22nm FinFET based 6T and 8T SRAMs weremade. The simulation of the SRAM model are carried out inGTS Framework TCAD tool based on 22nm technology. In8T SRAM cell, two n-FinFETs are added to the conventional6T SRAM cell which will be controlled by the Read WordLine (RWL) to isolate the read and write operation path forbetter read stability. FinFET based 8T SRAM cell givesbetter performance in Static Noise Margin (SNM) and powerconsumption than 6T SRAM cells. The simulation resultsaffirms the proposed FinFET based 8T SRAM improvedread static noise margin by 166.67% and power consumptionby 76.13% as compared to the FinFET based 6T SRAM

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