Face recognition using illumination-invariant local patches

Illumination variation that span globally and locally across the facial surface is one of the most important aspect in designing a robust face recognition system. The illumination variations due to changes in lighting conditions could produce different shape of shading on the face thus deforming the facial features. The effect of these variations is simply more severe in the presence of single-sample constraint since there would be many variables with very limited observations. Illumination variations have been modelled in literature as a series of undetermined multiplicative and additive noise, hence it is more convenient to eliminate or reduce the effect rather than computing them. In this paper, we present an illumination-invariant method where we use local features as basis for face classification which is obtained from partitioning histogram-equalized faces into smaller overlapping local patches (LPs). We can achieve illumination-invariance for these LPs by subtracting the vectors with local average illumination and then these vectors are logarithmically normalized to enhance the local contrast. The degree of invariance is controlled by a weight connected to the average intensity component. We have tested this method in single sample face recognition setting on AR Database and Extended YALE B Database. Recognition results show that the proposed method is suitable for robust face recognition since it achieve good performance in both even illumination and uneven illumination cases

Adaptive background reconstruction for street surveillance

In recent years, adaptive background reconstruction works have found interest in many researchers. However, the existing algorithms that have been proposed by other researchers still in the early stage of development and many aspects need to be improved. In this paper, an adaptive background reconstruction is presented. Past pixel observation is used. The proposed algorithm also has eliminated the need of the pre-training of non-moving objects in the background. The proposed algorithm is capable of reconstructing the background with moving objects in video sequence. Experimental results show that the proposed algorithms are able to reconstruct the background correctly and handle illumination and adverse weather that modifies the background

Car detection using cascade classifier on embedded platform

Advanced Driver-Assistance Systems (ADAS) help reducing traffic accidents caused by distracted driving. One of the features of ADAS is Forward Collision Warning System (FCWS). In FCWS, car detection is a crucial step. This paper explains about car detection system using cascade classifier running on embedded platform. The embedded platform used is NXP SBC-S32V234 evaluation board with 64-bit Quad ARM Cortex-A53. The system algorithm is developed in C++ programming language and used open source computer vision library, OpenCV. For car detection process, object detection by cascade classifier method is used. We trained the cascade detector using positive and negative instances mostly from our self-collected Malaysian road dataset. The tested car detection system gives about 88.3 percent detection accuracy with images of 340 by 135 resolution (after cropped and resized). When running on the embedded platform, it managed to get average 13 frames per second with video file input and average 15 frames per second with camera input

Autonomous navigation of mobile robot using kinect sensor

The problem of achieving real time process in depth camera application, in particular when used for indoor mobile robot localization and navigation is far from being solved. Thus, this paper presents autonomous navigation of the mobile robot by using Kinect sensor. By using Microsoft Kinect XBOX 360 as the main sensor, the robot is expected to navigate and avoid obstacles safely. By using depth data, 3D point clouds, filtering and clustering process, the Kinect sensor is expected to be able to differentiate the obstacles and the path in order to navigate safely. Therefore, this research requirement to propose a creation of low-cost autonomous mobile robot that can be navigated safely

Analysis on background subtraction for street surveillance

Background subtraction is a well-known technique used in computer vision applications. However, in public surveillance system, the utilization of background subtraction still new and far from being solved. Insufficient analysis of the background subtraction algorithms made the situation getting worse. The analysis of the commonly-used algorithms is presented in this paper. Experiments are conducted to quantitatively evaluate the performance of the algorithms by using three video sequences. The more suitable algorithm for various conditions is expected to be presented as the results in this paper

Intelligent surveillance system for street surveillance

CCTV surveillance systems are widely used as a street monitoring tool in public and private areas. This paper presents a novel approach of an intelligent surveillance system that consists of adaptive background modelling, optimal trade-off features tracking and detected moving objects classification. The proposed system is designed to work in real-time. Experimental results show that the proposed background modelling algorithms are able to reconstruct the background correctly and handle illumination and adverse weather that modifies the background. For the tracking algorithm, the effectiveness between colour, edge and texture features for target and candidate blobs were analysed. Finally, it is also demonstrated that the proposed object classification algorithm performs well with different classes of moving objects such as, cars, motorcycles and pedestrians

Development of load carrying and releasing system of hexacopter

An unmanned aerial vehicle (UAV) which is integrated with the gripper system is be able to provide diverse service to the society. The objective of this project is to develop a load carrying and releasing system for hexacopter platform. The proposed method is to use the hexacopter with high payload capable of flying while carrying some extra load. A gripper system with the ability to hold and release the preloaded object is designed to enable the hexacopter to transport objects. The load carrying and releasing system releases the object after receiving an instruction from the user. During the flight, mission planner is used to monitor the flight status. The hexacopter performance was evaluated through a series of flight tests. The result from the experiments show that the total flight time of the hexacopter reduced by 5 minutes from the normal time of 15 minutes when the load carrying mechanism is attached. This is because attaching the mechanism increases the total payload by 800grams from the normal payload of 2.2kg. Payload increment means higher current drawn from the battery to produce higher motor speed. Attaching the mechanism also affects the stabilization of the UAV. Hence, retuning of the PID controller was performed in order to correct the flight attitude of the UAV. This project successfully demonstrated the development of load carrying and releasing system which might serve as guidelines for other related future projects

Analysis of artificial neural network and viola-jones algorithm based moving object detection

In recent years, the worrying rate of street crime has demanded more reliable and efficient public surveillance system. Analysis of moving object detection methods is presented in this paper, includes Artificial Neural Network (ANN) and Viola-Jones algorithm. Both methods are compared based on their precision of correctly classify the moving objects. The emphasis is on two major issues involve in the analysis of moving object detection, and object classification to two groups, pedestrian and motorcycle. Experiments are conducted to quantitatively evaluate the performance of the algorithms by using two types of dataset, which are different in term of complexity of the background. The utilization of cascade architecture to the extracted features, benefits the algorithm. The algorithms have been tested on simulated events, and the more suitable algorithm with high detection rate is expected to be presented in this paper