Seyed Yahya Nikouei

Meng Yuan, Seyed Yahya Nikouei, Alem Fitwi et al.

The collection of a lot of personal information about individuals, including the minor members of a family, by closed-circuit television (CCTV) cameras creates a lot of privacy concerns. Particularly, revealing children's identifications or activities may compromise their well-being. In this paper, we investigate lightweight solutions that are affordable to edge surveillance systems, which is made feasible and accurate to identify minors such that appropriate privacy-preserving measures can be applied accordingly. State of the art deep learning architectures are modified and re-purposed in a cascaded fashion to maximize the accuracy of our model. A pipeline extracts faces from the input frames and classifies each one to be of an adult or a child. Over 20,000 labeled sample points are used for classification. We explore the timing and resources needed for such a model to be used in the Edge-Fog architecture at the edge of the network, where we can achieve near real-time performance on the CPU. Quantitative experimental results show the superiority of our proposed model with an accuracy of 92.1% in classification compared to some other face recognition based child detection approaches.

Seyed Yahya Nikouei, Yu Chen, Alexander Aved et al.

Situation AWareness (SAW) is essential for many mission critical applications. However, SAW is very challenging when trying to immediately identify objects of interest or zoom in on suspicious activities from thousands of video frames. This work aims at developing a queryable system to instantly select interesting content. While face recognition technology is mature, in many scenarios like public safety monitoring, the features of objects of interest may be much more complicated than face features. In addition, human operators may not be always able to provide a descriptive, simple, and accurate query. Actually, it is more often that there are only rough, general descriptions of certain suspicious objects or accidents. This paper proposes an Interactive Video Surveillance as an Edge service (I-ViSE) based on unsupervised feature queries. Adopting unsupervised methods that do not reveal any private information, the I-ViSE scheme utilizes general features of a human body and color of clothes. An I-ViSE prototype is built following the edge-fog computing paradigm and the experimental results verified the I-ViSE scheme meets the design goal of scene recognition in less than two seconds.

Seyed Yahya Nikouei, Yu Chen, Alexander Aved et al.

Urban imagery usually serves as forensic analysis and by design is available for incident mitigation. As more imagery collected, it is harder to narrow down to certain frames among thousands of video clips to a specific incident. A real-time, proactive surveillance system is desirable, which could instantly detect dubious personnel, identify suspicious activities, or raise momentous alerts. The recent proliferation of the edge computing paradigm allows more data-intensive tasks to be accomplished by smart edge devices with lightweight but powerful algorithms. This paper presents a forensic surveillance strategy by introducing an Instant Suspicious Activity identiFication at the Edge (I-SAFE) using fuzzy decision making. A fuzzy control system is proposed to mimic the decision-making process of a security officer. Decisions are made based on video features extracted by a lightweight Deep Machine Learning (DML) model. Based on the requirements from the first-line law enforcement officers, several features are selected and fuzzified to cope with the state of uncertainty that exists in the officers' decision-making process. Using features in the edge hierarchy minimizes the communication delay such that instant alerting is achieved. Additionally, leveraging the Microservices architecture, the I-SAFE scheme possesses good scalability given the increasing complexities at the network edge. Implemented as an edge-based application and tested using exemplary and various labeled dataset surveillance videos, the I-SAFE scheme raises alerts by identifying the suspicious activity in an average of 0.002 seconds. Compared to four other state-of-the-art methods over two other data sets, the experimental study verified the superiority of the I-SAFE decentralized method.

Seyed Yahya Nikouei, Ronghua Xu, Yu Chen et al.

Connected societies require reliable measures to assure the safety, privacy, and security of members. Public safety technology has made fundamental improvements since the first generation of surveillance cameras were introduced, which aims to reduce the role of observer agents so that no abnormality goes unnoticed. While the edge computing paradigm promises solutions to address the shortcomings of cloud computing, e.g., the extra communication delay and network security issues, it also introduces new challenges. One of the main concerns is the limited computing power at the edge to meet the on-site dynamic data processing. In this paper, a Lightweight IoT (Internet of Things) based Smart Public Safety (LISPS) framework is proposed on top of microservices architecture. As a computing hierarchy at the edge, the LISPS system possesses high flexibility in the design process, loose coupling to add new services or update existing functions without interrupting the normal operations, and efficient power balancing. A real-world public safety monitoring scenario is selected to verify the effectiveness of LISPS, which detects, tracks human objects and identify suspicious activities. The experimental results demonstrate the feasibility of the approach.

Deeraj Nagothu, Ronghua Xu, Seyed Yahya Nikouei et al.

While the smart surveillance system enhanced by the Internet of Things (IoT) technology becomes an essential part of Smart Cities, it also brings new concerns in security of the data. Compared to the traditional surveillance systems that is built following a monolithic architecture to carry out lower level operations, such as monitoring and recording, the modern surveillance systems are expected to support more scalable and decentralized solutions for advanced video stream analysis at the large volumes of distributed edge devices. In addition, the centralized architecture of the conventional surveillance systems is vulnerable to single point of failure and privacy breach owning to the lack of protection to the surveillance feed. This position paper introduces a novel secure smart surveillance system based on microservices architecture and blockchain technology. Encapsulating the video analysis algorithms as various independent microservices not only isolates the video feed from different sectors, but also improve the system availability and robustness by decentralizing the operations. The blockchain technology securely synchronizes the video analysis databases among microservices across surveillance domains, and provides tamper proof of data in the trustless network environment. Smart contract enabled access authorization strategy prevents any unauthorized user from accessing the microservices and offers a scalable, decentralized and fine-grained access control solution for smart surveillance systems.

Seyed Yahya Nikouei, Ronghua Xu, Deeraj Nagothu et al.

Information from surveillance video is essential for situational awareness (SAW). Nowadays, a prohibitively large amount of surveillance data is being generated continuously by ubiquitously distributed video sensors. It is very challenging to immediately identify the objects of interest or zoom in suspicious actions from thousands of video frames. Making the big data indexable is critical to tackle this problem. It is ideal to generate pattern indexes in a real-time, on-site manner on the video streaming instead of depending on the batch processing at the cloud centers. The modern edge-fog-cloud computing paradigm allows implementation of time sensitive tasks at the edge of the network. The on-site edge devices collect the information sensed in format of frames and extracts useful features. The near-site fog nodes conduct the contextualization and classification of the features. The remote cloud center is in charge of more data intensive and computing intensive tasks. However, exchanging the index information among devices in different layers raises security concerns where an adversary can capture or tamper with features to mislead the surveillance system. In this paper, a blockchain enabled scheme is proposed to protect the index data through an encrypted secure channel between the edge and fog nodes. It reduces the chance of attacks on the small edge and fog devices. The feasibility of the proposal is validated through intensive experimental analysis.

Seyed Yahya Nikouei, Yu Chen, Sejun Song et al.

Edge computing efficiently extends the realm of information technology beyond the boundary defined by cloud computing paradigm. Performing computation near the source and destination, edge computing is promising to address the challenges in many delay-sensitive applications, like real-time human surveillance. Leveraging the ubiquitously connected cameras and smart mobile devices, it enables video analytics at the edge. In recent years, many smart video surveillance approaches are proposed for object detection and tracking by using Artificial Intelligence (AI) and Machine Learning (ML) algorithms. This work explores the feasibility of two popular human-objects detection schemes, Harr-Cascade and HOG feature extraction and SVM classifier, at the edge and introduces a lightweight Convolutional Neural Network (L-CNN) leveraging the depthwise separable convolution for less computation, for human detection. Single Board computers (SBC) are used as edge devices for tests and algorithms are validated using real-world campus surveillance video streams and open data sets. The experimental results are promising that the final algorithm is able to track humans with a decent accuracy at a resource consumption affordable by edge devices in real-time manner.

Seyed Yahya Nikouei, Yu Chen, Sejun Song et al.

Edge computing allows more computing tasks to take place on the decentralized nodes at the edge of networks. Today many delay sensitive, mission-critical applications can leverage these edge devices to reduce the time delay or even to enable real time, online decision making thanks to their onsite presence. Human objects detection, behavior recognition and prediction in smart surveillance fall into that category, where a transition of a huge volume of video streaming data can take valuable time and place heavy pressure on communication networks. It is widely recognized that video processing and object detection are computing intensive and too expensive to be handled by resource limited edge devices. Inspired by the depthwise separable convolution and Single Shot Multi-Box Detector (SSD), a lightweight Convolutional Neural Network (LCNN) is introduced in this paper. By narrowing down the classifier's searching space to focus on human objects in surveillance video frames, the proposed LCNN algorithm is able to detect pedestrians with an affordable computation workload to an edge device. A prototype has been implemented on an edge node (Raspberry PI 3) using openCV libraries, and satisfactory performance is achieved using real world surveillance video streams. The experimental study has validated the design of LCNN and shown it is a promising approach to computing intensive applications at the edge.