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

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 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

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

Detection of different classes moving object in public surveillance using artificial neural network (ANN)

Public surveillance monitoring is rapidly finding its way into Intelligent Surveillance Systems. Street crimes such as snatch theft is increasing drastically in recent years, cause a serious threat to human life worldwide. In this paper, a moving object detection and classification model was developed using novel Artificial Neural Network (ANN) simulation with the aim to identify its suitability for different classes of moving objects, particularly in public surveillance conditions. The result demonstrated that the proposed method consistently performs well with different classes of moving objects such as, motorcyclist, and pedestrian. Thus, it is reliable to detect different classes of moving object in public surveillance camera. It is also computationally fast and applicable for detecting moving objects in real-time

Moving object detection and classification using neuro-fuzzy approach

Public surveillance monitoring is rapidly finding its way into Intelligent Surveillance System. Street crime is increasing in recent years, which has demanded more reliable and intelligent public surveillance system. In this paper, the ability and the accuracy of an Adaptive Neuro-Fuzzy Inference System (ANFIS) was investigated for the classification of moving objects for street scene applications. The goal of this paper is to classify the moving objects prior to its communal attributes that emphasize on three major processes which are object detection, discriminative feature extraction, and classification of the target. The intended surveillance application would focus on street scene, therefore the target classes of interest are pedestrian, motorcyclist, and car. The adaptive network based on Neuro-fuzzy was independently developed for three output parameters, each of which constitute of three inputs and 27 Sugeno-rules. Extensive experimentation on significant features has been performed and the evaluation performance analysis has been quantitatively conducted on three street scene dataset, which differ in terms of background complexity. Experimental results over a public dataset and our own dataset demonstrate that the proposed technique achieves the performance of 93.1% correct classification for street scene with moving objects, with compared to the solely approaches of neural network or fuzzy

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

Features selection for multi-camera tracking

Snatch theft is becoming more prevalent in Malaysia nowadays and proper measures must be taken to reduce it. CCTV surveillance systems have been widely used as a street crime prevention tool across public and private areas. Tracking the same object within different cameras' view is essential in many surveillance applications. Recently, most of the researchers have grown more interest on how to track objects across cameras due to the increasing number of cameras. However, the current approach proposed by the researchers still offer trade-off in terms of its accuracy and speed. As the tracking accuracy increases, the speed will decrease that acts reversely proportional to it. This paper presents a novel approach to track moving objects across distributed cameras that provides the most optimal trade-off based on color, texture and edge features. The color, edge and texture features for target and candidate blobs are computed by a novel computational algorithm. This study focuses on analyzing of video surveillance in public places, specifically in outdoor environment. In the result section, the comparison between the effectiveness of the features used in the tracking algorithm is presented. The performance of the method is analyzed based on its accuracy and speed. The more suitable features are identified. Experimental results show the effectiveness of this method for real-time operation

Real-time tracking using edge and color feature

Visual surveillance system is an important tool used for monitoring a scene in order to detect any suspicious behavior. Recently, due to the increasing number of cameras, most of the researchers have shown more interest on how to track objects across the cameras. However, the existing algorithms that have been proposed by earlier researchers still offer some trade-off. As the tracking accuracy increases, the speed will be affected and vice-versa. Thus, this paper presents a novel approach for tracking moving objects which provides the most optimal trade-off in terms of its accuracy and speed. In this paper, a novel computational algorithm for dominant color to deal with the changes of objects' viewpoints is presented. Though, color cue alone is insufficient to provide a reliable tracking since in realistic environment, specifically outdoors, variation of illumination may affect the object appearance. Additional feature that is invariant to this imaging feature, namely edge is used in this project to compensate with the shortcoming. The tracking performance of the algorithm based on each individual feature and the fusion of the edge and color features are presented. Experimental results show that the proposed algorithm is reliable for real-time operation