Security and the smart city: A systematic review

Video surveillance systems obtain a great interest as application-oriented studies that have been growing rapidly in the past decade. The most recent studies attempt to integrate computer vision, image processing, and artificial intelligence capabilities into video surveillance applications. Although there are so many achievements in the acquisition of datasets, methods, and frameworks published, there are not many papers that can provide a comprehensive picture of the current state of video surveillance system research. This paper provides a comprehensive and systematic review on the literature from various video surveillance system studies published from 2010 through 2019. Within a selected study extraction process, 220 journal-based publications were identified and analyzed to illustrate the research trends, datasets, methods, and frameworks used in the field of video surveillance, to provide an in-depth explanation about research trends that many topics raised by researchers as a focus in their researches, to provide references on public datasets that are often used by researchers as a comparison and a means of developing a test method, and to give accounts on the improvement and integration of network infrastructure design to meet the demand for multimedia data. In the end of this paper, several opportunities and challenges related to researches in the video surveillance system are mentioned.

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A Systematic Review of Intelligence Video Surveillance: Trends, Techniques, Frameworks, and Datasets

Anomaly detection is an important issue in trajectory data mining. Various approaches have been proposed to address this issue. However, most previous studies focus only on outlier detection but rarely on pattern mining of anomalous trajectories. Mining patterns of anomalous trajectories can reveal the underlying mechanisms of these outliers. This paper studies four distinct patterns of anomalous trajectories, and proposes a method to detect and classify them. First, we present the difference and intersection set (DIS) distance metric to evaluate the similarity between any two trajectories. Based on this distance, we design an anomaly score function to quantify the differences between different types of anomalous trajectories and normal trajectories. We further propose an anomalous trajectory detection and classification (ATDC) method to find anomalies in different anomalous patterns. Finally, we evaluate the proposed ATDC method through extensive experiments on real cab trajectory data. The results show that the proposed approach outperforms existing methods by a significant margin.

Anomalous Trajectory Detection and Classification Based on Difference and Intersection Set Distance

Detecting abnormal trajectories is an important task in research and industrial applications, which has attracted considerable attention in recent decades. This work studies the existing trajectory outlier detection algorithms in different industrial domains and applications, including maritime, smart urban transportation, video surveillance, and climate change domains. First, we review several algorithms for trajectory outlier detection. Second, different taxonomies are proposed regarding application-, output-, and algorithm-based levels. Third, evaluation of 10 trajectory outlier detection algorithms is performed on small, large, and big trajectory databases. Finally, future challenges and open issues with regard to trajectory outliers are derived and discussed. This survey offers a general overview of existing trajectory outlier detection algorithms in industrial informatics applications. As a result, mature solutions may be further developed by data mining and machine learning communities.

Trajectory Outlier Detection: Algorithms, Taxonomies, Evaluation, and Open Challenges

Nowadays, massive data has been brought by the rapid development of technology. When finding whether trajectory to be detected is abnormal under the premise of given normal trajectories, we innovatively propose 1) Seq2Seq model based on LSTM prediction network for trajectory modelling (SL-Modelling), and 2) abnormal trajectory detection method with spatio-temporal and semantic information. Firstly, SL-Modelling is used to obtain sequence-type trajectory models of normal trajectory groups directly for subsequent detection with no need to extract a large number of features manually and adapting to different sequence length. Then we introduce the concept of distance and semantic interest sequence that makes full use of spatio-temporal and semantic information of trajectories. Finally, the similarity between models and trajectory to be detected is calculated to detect abnormal trajectory. The experimental results of publicly available flight data set show that trajectory models obtained are descriptive enough to represent normal trajectory groups well, and the accuracy of modelling is higher than the existing advanced methods. Besides, the detection with spatio-temporal and semantic information has been verified that it has stronger detection ability with higher accuracy and takes less time.

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A Method for LSTM-Based Trajectory Modeling and Abnormal Trajectory Detection

This paper presents an original method to detect anomalous human trajectories based on a new and promising context-aware distance. The input of the proposed method is a set of human trajectories from a video surveillance system. A proper representation of each trajectory is developed based on the polar coordinates of the corresponding sub-trajectories. The main focus of the paper is to highlight a context-aware distance between trajectories. This distance implies a weighted average of the differences in the angle, the Euclidean distance, and the number of points in each trajectory. The distance matrix feeds an unsupervised learning method to extract homogeneous groups (clusters) of trajectories. Finally, an outlier detection method is executed over the trajectories in each cluster. The methodology has been empirically evaluated across four experiments with both artificial and real data sets. The tests results have proved promising and illustrate the effectiveness of this approach for anomalous trajectories detection.

Outlier trajectory detection through a context-aware distance

In this paper, a novel methodology for the representation and distance measurement of trajectories is introduced in order to perform outliers detection tasks. First, a features extraction procedure based on the linear segmentation of trajectories is presented. Next, a configurable context-aware distance is defined. Our representation and distance are significant in that they weigh the relative importance of several relevant features of the trajectories. A clustering method is applied based on the distances matrix and the outliers detection task is performed in any of the clusters. The results of the experiments show the good performance of the method when applied in two different real data sets.

Context-Aware Distance for Anomalous Human Trajectories Detection

This paper presents a novel distributed intelligent video surveillance architecture based on Wireless Multimedia Sensor Networks (WMSNs). This architecture is part of a video surveillance project and has been built using the Robot Operating System (ROS). ROS allows to develop and connect (through wireless TCP-IP) several modules to process and manage multimedia data in an easy way. The real-time intelligent surveillance system has been trained for detecting, tracking and monitoring people and vehicles in an indoor-outdoor real environment. The test process shows the reliability of the developed system as a tool for the identification of security incidents. Besides, using wireless connections and a distributed architecture together, we have achieved a really flexible, easy to install and lower-maintenance system that supports many different devices. Thus, the proposed architecture can be applied in distributed locations such as smart cities.

Scalable and flexible wireless distributed architecture for intelligent video surveillance systems

A novel method for re-identification based on optimal features extraction in VideoSurveillance environments is presented in this paper. A high number of features are extracted for each detected person in a dataset obtained from a camera in a scenario. An evaluation of the relative discriminate power of each bag of features for each person is performed. We propose a forward method in a Support Vector framework to obtained the optimal individual bags of features. These bags of features are used in a new scenario in order to detect suspicious persons using the images from a non-overlapping camera. The results obtained demonstrate the promising potential of the presented approach. The proposed method can be enriched with future enhancements that can further improve its effectiveness and complexity in more complex VideoSurveillance situations.

Ignacio San Román

Papers

Outlier trajectory detection through a context-aware distance

Context-Aware Distance for Anomalous Human Trajectories Detection

Scalable and flexible wireless distributed architecture for intelligent video surveillance systems

WYA2: Optimal Individual Features Extraction for VideoSurveillance Re-identification.