Context Of Video Surveillance Research Articles

Semi-Supervised Anomaly Detection in Video-Surveillance Scenes in the Wild.

Surveillance cameras are being installed in many primary daily living places to maintain public safety. In this video-surveillance context, anomalies occur only for a very short time, and very occasionally. Hence, manual monitoring of such anomalies may be exhaustive and monotonous, resulting in a decrease in reliability and speed in emergency situations due to monitor tiredness. Within this framework, the importance of automatic detection of anomalies is clear, and, therefore, an important amount of research works have been made lately in this topic. According to these earlier studies, supervised approaches perform better than unsupervised ones. However, supervised approaches demand manual annotation, making dependent the system reliability of the different situations used in the training (something difficult to set in anomaly context). In this work, it is proposed an approach for anomaly detection in video-surveillance scenes based on a weakly supervised learning algorithm. Spatio-temporal features are extracted from each surveillance video using a temporal convolutional 3D neural network (T-C3D). Then, a novel ranking loss function increases the distance between the classification scores of anomalous and normal videos, reducing the number of false negatives. The proposal has been evaluated and compared against state-of-art approaches, obtaining competitive performance without fine-tuning, which also validates its generalization capability. In this paper, the proposal design and reliability is presented and analyzed, as well as the aforementioned quantitative and qualitative evaluation in-the-wild scenarios, demonstrating its high sensitivity in anomaly detection in all of them.

High-level surveillance event detection

Security has been raised at major public buildings in the most famous and crowded cities all over the world following the terrorist attacks of the last years, the latest one at the Promenade des Anglais in Nice. For that reason, video surveillance systems have become more and more essential for detecting and hopefully even prevent dangerous events in public areas. In this work, we present an overview of the evolution of high-level surveillance event detection systems along with a prototype for anomaly detection in video surveillance context. The whole process is described, starting from the video frames captured by sensors/cameras till at the end some well-known reasoning algorithms for finding potentially dangerous activities are applied.

Efficient Resource Management for Cloud-enabled Video Surveillance over Next Generation Network

The next generation video surveillance systems are expected to face challenges in providing computation support for an unprecedented amount of video streams from multiple video cameras in a timely and scalable fashion. Cloud computing offers huge computation resources for large-scale storage and processing on demand, which are deemed suitable for video surveillance tasks. Cloud also provides quality of service guaranteed hardware and software solutions with the virtual machine (VM) technology using a utility-like service costing model. In cloud-based video surveillance context, the resource requests to handle video surveillance tasks are translated in the form of VM resource requests, which in turn are mapped to VM resource allocation referring to physical server resources hosting the VMs. Due to the nature of video surveillance tasks, these requests are highly time-constrained, heterogeneous and dynamic in nature. Hence, it is very challenging to actually manage the cloud resources from the perspective of VM resource allocation given the stringent requirements of video surveillance tasks. This paper proposes a computation model to efficiently manage cloud resources for surveillance tasks allocation. The proposed model works on optimizing the trade-off between average service waiting time and long-term service cost, and shows that long-term service cost is inversely proportional to high and balanced utilization of cloud resources. Experiments show that our approach provides a near-optimal solution for cloud resource management when handling the heterogeneous and unpredictable video surveillance tasks dynamically over next generation network.

Secure privacy vault design for distributed multimedia surveillance system

Distributed multimedia surveillance systems utilize heterogeneous sensors such as cameras, motion sensors, sound sensors, and RFID in order to provide safety and security to people. However, due to the potential of exposing privacy by these systems, many people are reluctant to be electronically monitored and suffer from privacy loss. In order to overcome this dilemma, the current surveillance systems should adopt improved privacy preservation (i.e. hiding people’s face) mechanism while they are used for typical surveillance tasks. This paper takes a holistic approach to identify the different privacy leakage channels in the distributed video surveillance context and proposes the design of a secure privacy vault to conceal privacy-sensitive data obtained from distributed visual sensors. It also shows how the proposed solution helps to mitigate the potential privacy leakage problems at different levels of the leakage channels. In order to demonstrate the viability of the proposed approach, we further provide the privacy leakage attack model as well as the security analysis of the proposed solution.

Shadow elimination and vehicles classification approaches in traffic video surveillance context

Video surveillance on highway is a hot topic and a great challenge in Intelligent Transportation Systems. In such applications requiring objects extraction, cast shadows induce shape distortions and object fusions interfering performance of high level algorithms. Shadow elimination allows to improve the performances of video object extraction, classification and tracking. In other hand, it is very important to recognize the type of a detected object in order to track reliably and estimate traffic parameters correctly. This paper presents two approaches to enhance automatic traffic surveillance systems. The first deals with the elimination of shadows and the second concerns the classification of vehicles, based on robust vision and image processing. For moving shadow elimination, a contrast model is proposed to describe and remove dynamic shadows based on the idea that a shadow transformation is a change in contrast. For vehicles classification, Hu moments are calculated in a manner to reduce the perspective effects and used to describe vehicles in knowledge base. Experimental results on the various challenging video sequences show that the proposed approach outperforms classification methods of related works (with a classification accuracy of 96.96%), and that the shadow elimination approach performs better than compared works (with detection rate of 95–99% and discrimination rate of 85.7–89%).