A Large-scale Distributed Video Parsing and Evaluation Platform

Visual surveillance systems have become one of the largest data sources of Big Visual Data in real world. However, existing systems for video analysis still lack the ability to handle the problems of scalability, expansibility and error-prone, though great advances have been achieved in a number of visual recognition tasks and surveillance applications, e.g., pedestrian/vehicle detection, people/vehicle counting. Moreover, few algorithms explore the specific values/characteristics in large-scale surveillance videos. To address these problems in large-scale video analysis, we develop a scalable video parsing and evaluation platform through combining some advanced techniques for Big Data processing, including Spark Streaming, Kafka and Hadoop Distributed Filesystem (HDFS). Also, a Web User Interface is designed in the system, to collect users' degrees of satisfaction on the recognition tasks so as to evaluate the performance of the whole system. Furthermore, the highly extensible platform running on the long-term surveillance videos makes it possible to develop more intelligent incremental algorithms to enhance the performance of various visual recognition tasks.Comment: Accepted by Chinese Conference on Intelligent Visual Surveillance 201

Video analytics system for surveillance videos

Developing an intelligent inspection system that can enhance the public safety is challenging. An efficient video analytics system can help monitor unusual events and mitigate possible damage or loss. This thesis aims to analyze surveillance video data, report abnormal activities and retrieve corresponding video clips. The surveillance video dataset used in this thesis is derived from ALERT Dataset, a collection of surveillance videos at airport security checkpoints. The video analytics system in this thesis can be thought as a pipelined process. The system takes the surveillance video as input, and passes it through a series of processing such as object detection, multi-object tracking, person-bin association and re-identification. In the end, we can obtain trajectories of passengers and baggage in the surveillance videos. Abnormal events like taking away other's belongings will be detected and trigger the alarm automatically. The system could also retrieve the corresponding video clips based on user-defined query

Multiple Moving Object Recognitions in video based on Log Gabor-PCA Approach

Object recognition in the video sequence or images is one of the sub-field of computer vision. Moving object recognition from a video sequence is an appealing topic with applications in various areas such as airport safety, intrusion surveillance, video monitoring, intelligent highway, etc. Moving object recognition is the most challenging task in intelligent video surveillance system. In this regard, many techniques have been proposed based on different methods. Despite of its importance, moving object recognition in complex environments is still far from being completely solved for low resolution videos, foggy videos, and also dim video sequences. All in all, these make it necessary to develop exceedingly robust techniques. This paper introduces multiple moving object recognition in the video sequence based on LoG Gabor-PCA approach and Angle based distance Similarity measures techniques used to recognize the object as a human, vehicle etc. Number of experiments are conducted for indoor and outdoor video sequences of standard datasets and also our own collection of video sequences comprising of partial night vision video sequences. Experimental results show that our proposed approach achieves an excellent recognition rate. Results obtained are satisfactory and competent.Comment: 8,26,conferenc

Advance Intelligent Video Surveillance System (AIVSS): A Future Aspect

Over the last few decades, remarkable infrastructure growths have been noticed in security-related issues throughout the world. So, with increased demand for Security, Video-based Surveillance has become an important area for the research. An Intelligent Video Surveillance system basically censored the performance, happenings, or changing information usually in terms of human beings, vehicles or any other objects from a distance by means of some electronic equipment (usually digital camera). The scopes like prevention, detection, and intervention which have led to the development of real and consistent video surveillance systems are capable of intelligent video processing competencies. In broad terms, advanced video-based surveillance could be described as an intelligent video processing technique designed to assist security personnel’s by providing reliable real-time alerts and to support efficient video analysis for forensic investigations. This chapter deals with the various requirements for designing a robust and reliable video surveillance system. Also, it is discussed the different types of cameras required in different environmental conditions such as indoor and outdoor surveillance. Different modeling schemes are required for designing of efficient surveillance system under various illumination conditions

Semantic web technologies for video surveillance metadata

Video surveillance systems are growing in size and complexity. Such systems typically consist of integrated modules of different vendors to cope with the increasing demands on network and storage capacity, intelligent video analytics, picture quality, and enhanced visual interfaces. Within a surveillance system, relevant information (like technical details on the video sequences, or analysis results of the monitored environment) is described using metadata standards. However, different modules typically use different standards, resulting in metadata interoperability problems. In this paper, we introduce the application of Semantic Web Technologies to overcome such problems. We present a semantic, layered metadata model and integrate it within a video surveillance system. Besides dealing with the metadata interoperability problem, the advantages of using Semantic Web Technologies and the inherent rule support are shown. A practical use case scenario is presented to illustrate the benefits of our novel approach

Advanced Video-Based Surveillance

Over the past decade, we have witnessed a tremendous growth in the demand for personal security and defense of vital infrastructure throughout the world. At the same time, rapid advances in video-based surveillance have emerged and offered a strategic technology to address the demands imposed by security applications. These events have led to a massive research effort devoted to the development of effective and reliable surveillance systems endowed with intelligent video-processing capabilities. As a result, advanced video-based surveillance systems have been developed by research groups from academia and industry alike. In broad terms, advanced video-based surveillance could be described as intelligent video processing designed to assist security personnel by providing reliable real-time alerts and to support efficient video analysis for forensics investigations