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

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