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

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

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

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

Features-based moving objects tracking for smart video surveillances: A review

Video surveillance is one of the most active research topics in the computer vision due to the increasing need for security. Although surveillance systems are getting cheaper, the cost of having human operators to monitor the video feed can be very expensive and inefficient. To overcome this problem, the automated visual surveillance system can be used to detect any suspicious activities that require immediate action. The framework of a video surveillance system encompasses a large scope in machine vision, they are background modelling, object detection, moving objects classification, tracking, motion analysis, and require fusion of information from the camera networks. This paper reviews recent techniques used by researchers for detection of moving object detection and tracking in order to solve many surveillance problems. The features and algorithms used for modelling the object appearance and tracking multiple objects in outdoor and indoor environment are also reviewed in this paper. This paper summarizes the recent works done by previous researchers in moving objects tracking for single camera view and multiple cameras views. Nevertheless, despite of the recent progress in surveillance technologies, there still are challenges that need to be solved before the system can come out with a reliable automated video surveillance

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

Real-time moving objects tracking for distributed smart video surveillances

Tracking the object of interest within a camera's view is essential for crime prevention. This study focuses on analyzing video surveillance in public places. It presents a novel approach to track moving objects across non-overlapping cameras' views that is able to give a consistent label to the objects throughout the whole multi-camera system in real-time. The proposed algorithm is also expected to be able to handle common problems in multiple-camera object tracking including variation of poses, object appearances and occlusion problems. The proposed algorithm was formulated based on visual and temporal cues for multiple cameras using entering/exiting and merging/splitting cases to deal with appearance changes and occlusion problems. Spatial cues are adopted in single-camera object tracking for real-time performance. A novel object segmentation technique based on the observed mask binary value is presented to deal with pose variation across different cameras. In the result section, the comparison between past works and the proposed tracking algorithm are presented. The experimental result

In silico characterization of UGT74G1 protein in Stevia rebaudiana Bertoni Accession MS007

Stevia rebaudiana is being promoted as an alternative sweetener in particular for diabetic and obese patients due to its low-calorie property. The steady demand in the market for high-quality stevia extracts presents a challenge for enhanced production of steviol glycosides that are safe for human consumption. This study characterized the structure and content of gene involved in the production of UGT74G1 protein for S. rebaudiana accession MS007 through in silico analysis using transcriptome dataset of stevia MS007. Homologous search using BLASTp show high similarity to Q6VAA6 RecName: Full=UDP-glycosyltransferase 74G1 (S. rebaudiana) as the top hit sequences. InterPro family and domain protein motif search revealed the presence and entry of IPR002213 and IPR035595. The construction of the phylogenetic tree was done by selecting 19 out of 102 protein sequences from BLASTp. The phylogenetic analysis showed the same protein family which is Asteraceae. ProtParam Ex-Pasy, PSIPRED and Phyre2 computed the primary, secondary, and tertiary structures for UGT74G1 protein. The UGT74G1 predicted tertiary structure scored 100.0% confidence by the single highest scoring template and coverage of 96%. The model has dimensions (Å) of X: 57.609, Y: 70.386, and Z: 58.351. Outcomes of this research will help to enhance the understanding of UDP-glycosyltransferase 74G1 (S. rebaudiana MS007) characteristic and enhance target identification processes to improve understanding of protein-protein interaction in S. rebaudiana MS007

Adaptive background modeling for dynamics background

An increasing number of CCTV have been deployed in public and crime-prone areas as demand for automatic monitoring system is increasing to counterbalance the limitation of human monitoring. To have a good monitoring system in such places, a good background model is needed in order to reduce amount of the video processing needed for tracking, classification, counting and etc. This paper proposes an adaptive background modeling that is able to model a scene under review at real-time. The proposed modeling system is also expected to be able to handle dynamic backgrounds and common problems in detection methods. A novel patch-based background reconstruction based on highest frequency of occurrences assumption and past pixel observation is proposed. Contrast adjusting method is used to reduce the problem of incorrectly classified foreground which is shadow problem. The proposed algorithm is focused to be tested and analytically compared with the dynamic background at the indoor and outdoor environment. The main challenges of background subtraction such as illumination changes, geometrical changes, stationary moving object problem and high speed object problem are taken care of and extensively discussed in this paper. The experimental results show that the algorithm is able to reconstruct a background model and produce accurate and precise foreground that can be used for other processing stages