Novel concepts and challenges for the next generation of video surveillance systems
31 May 2007-Vol. 18, Iss: 3, pp 135-137
TL;DR: Long gone are the days of a video surveillance system capable of processing only one video stream acquired by a single fixed camera, where operators expect the real-time description of scene evolution in natural language of any type of expected and unexpected event.
Abstract: Long gone are the days of a video surveillance system capable of processing only one video stream acquired by a single fixed camera. In those days algorithms were tested in laboratory environments, with a small number of people moving orderly and with limited clutter in the scene. Modern monitoring systems have much more demanding requirements: large, busy and complex scenes, the use of heterogeneous sensor networks, the real-time acquisition and interpretation of the evolving scene; instantaneous flagging of potentially critical situations in any weather and illumination conditions. Moreover, operators expect the real-time description of scene evolution in natural language of any type of expected and unexpected event, involving a variety of situations, from nobody in the scene to groups of people, and in some cases very crowded environments. The monitoring of public and private spaces has become a necessity, because of the steady increase in
Citations
More filters
TL;DR: The methods reviewed are intended for real-time surveillance through definition of a diverse set of events for further analysis triggering, including virtual fencing, speed profiling, behavior classification, anomaly detection, and object interaction.
Abstract: This paper presents a survey of trajectory-based activity analysis for visual surveillance. It describes techniques that use trajectory data to define a general set of activities that are applicable to a wide range of scenes and environments. Events of interest are detected by building a generic topographical scene description from underlying motion structure as observed over time. The scene topology is automatically learned and is distinguished by points of interest and motion characterized by activity paths. The methods we review are intended for real-time surveillance through definition of a diverse set of events for further analysis triggering, including virtual fencing, speed profiling, behavior classification, anomaly detection, and object interaction.
528 citations
Cites background from "Novel concepts and challenges for t..."
...Automatic behavior understanding from video is a very challenging problem [1]....
[...]
...New exciting work has come forward to analyze interactions between humans, vehicles, and infrastructure, allowing monitoring of suspicious meetings [1] and luggage drops as well as characterization of conflicts for road safety [12]–[15]....
[...]
TL;DR: This work analyzes the existing challenges in video-based surveillance systems for the vehicle and presents a general architecture for video surveillance systems, i.e., the hierarchical and networked vehicle surveillance, to survey the different existing and potential techniques.
Abstract: Traffic surveillance has become an important topic in intelligent transportation systems (ITSs), which is aimed at monitoring and managing traffic flow. With the progress in computer vision, video-based surveillance systems have made great advances on traffic surveillance in ITSs. However, the performance of most existing surveillance systems is susceptible to challenging complex traffic scenes (e.g., object occlusion, pose variation, and cluttered background). Moreover, existing related research is mainly on a single video sensor node, which is incapable of addressing the surveillance of traffic road networks. Accordingly, we present a review of the literature on the video-based vehicle surveillance systems in ITSs. We analyze the existing challenges in video-based surveillance systems for the vehicle and present a general architecture for video surveillance systems, i.e., the hierarchical and networked vehicle surveillance, to survey the different existing and potential techniques. Then, different methods are reviewed and discussed with respect to each module. Applications and future developments are discussed to provide future needs of ITS services.
167 citations
01 Jul 2013
TL;DR: A small-size background model is built by the proposed single spatio-temporal distributed Gaussian model, whose size is the same as input frame, to decrease computation time and memory storage without loss of detection performance.
Abstract: This paper presents a fast and reliable method for moving object detection with moving cameras (including pan---tilt---zoom and hand-held cameras). Instead of building large panoramic background model as conventional approaches, we construct a small-size background model, whose size is the same as input frame, to decrease computation time and memory storage without loss of detection performance. The small-size background model is built by the proposed single spatio-temporal distributed Gaussian model and this can solve false detection results arising from registration error and background adaptation problem in moving background. More than the proposed background model based on spatial and temporal information, several pre- and post-processing methods are adopted and organized systematically to enhance the detection performances. We evaluate the proposed method with several video sequences under difficult conditions, such as illumination change, large zoom variation, and fast camera movement, and present outperforming detection results of our algorithm with fast computation time.
96 citations
Cites background from "Novel concepts and challenges for t..."
...In these days, monitoring of public and private spaces is required because of the steady increase in crimes and safety issues [17]....
[...]
20 Oct 2009
TL;DR: The focus of this paper is on the development of novel solutions to achieve a measurable level of dependability for the security system in order to fulfill the requirements of the specific application.
Abstract: Rail-based mass transit systems are vulnerable to many criminal acts, ranging from vandalism to terrorism. In this paper, we present the architecture, the main functionalities and the dependability related issues of a security system specifically tailored to metro railways. Heterogeneous intrusion detection, access control, intelligent video-surveillance and sound detection devices are integrated in a cohesive Security Management System (SMS). In case of emergencies, the procedural actions required to the operators involved are orchestrated by the SMS. Redundancy both in sensor dislocation and hardware apparels (e.g. by local or geographical clustering) improve detection reliability, through alarm correlation, and overall system resiliency against both random and malicious threats. Video-analytics is essential, since a small number of operators would be unable to visually control a large number of cameras. Therefore, the visualization of video streams is activated automatically when an alarm is generated by smart-cameras or other sensors, according to an event-driven approach. The system is able to protect stations (accesses, technical rooms, platforms, etc.), tunnels (portals, ventilation shafts, etc.), trains and depots. Presently, the system is being installed in the Metrocampania underground regional railway. To the best of our knowledge, this is the first subway security system featuring artificial intelligence algorithms both for video and audio surveillance. The security system is highly heterogeneous in terms not only of detection technologies but also of embedded computing power and communication facilities. In fact, sensors can differ in their inner hardware-software architecture and thus in the capacity of providing information security and dependability. The focus of this paper is on the development of novel solutions to achieve a measurable level of dependability for the security system in order to fulfill the requirements of the specific application.
54 citations
TL;DR: A policy for mutual help among MEC UAVs is defined in order to increase the performance of the whole aerial MEC platform, so further reducing end-to-end latency between sources and actuators, and increasing system reliability.
Abstract: In the last decade, video surveillance systems have become more and more popular. Thanks to a decrease in price of video camera devices and the diffusion of cheap small unmanned aerial vehicles (UAVs), video monitoring is today adopted in a wide range of application cases, from road traffic control to precision agriculture. This leads to capture a great amount of visual material to be monitored and screened for event detection. However, information that is gathered from a platform of video monitoring UAVs may produce high-volume data, whose processing is unfeasible to be done locally by the same UAVs that perform monitoring. Moreover, because of the limited bandwidth of wireless links connecting UAVs to computing infrastructures that are installed on ground, offloading these data to edge clouds renders these platforms infeasible for video analysis applications with low-latency requirements. The target of this paper is to extend a 5G network slice for video monitoring with a Flying Ad-hoc NETwork (FANET) constituted by UAVs with multi-access edge computing (MEC) facilities (MEC UAVs), flying very close to the layer of UAVs monitoring the area of interest. A policy for mutual help among MEC UAVS is defined in order to increase the performance of the whole aerial MEC platform, so further reducing end-to-end latency between sources and actuators, and increasing system reliability. A use case is considered for a numerical analysis of the proposed platform.
38 citations