Finding Abnormal Vessel Trajectories Using Feature Learning

Fu, Peiguo; Wang, Haozhou; Liu, Kuien; Hu, Xiaohui; Zhang, Hui

doi:10.1109/access.2017.2698208

Cited by 48 publications

(29 citation statements)

References 29 publications

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“…Moreover, the graph-based representation and learning of relevant features are combined and correlated with target behaviors to detect abnormalities in moving object trajectories, so as to determine whether the events of interest are normal or abnormal [24]. Fu et al [25] utilize reference points as well as the piecewise linear segmentation algorithm to compress the trajectories, and then propose a time-aware and spatially correlated collaborative algorithm to increase the density of the trajectories to improve the accuracy of abnormal event detection. However, in this method there exists the issue of large cumulative errors in trajectory calculation.…”

Section: A Hand-crafted Features-based Modelsmentioning

confidence: 99%

Abnormal Event Detection via Feature Expectation Subgraph Calibrating Classification in Video Surveillance Scenes

Deng

et al. 2020

IEEE Access

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At present, the existing abnormal event detection models based on deep learning mainly focus on data represented by a vectorial form, which pay little attention to the impact of the internal structure characteristics of feature vector. In addition, a single classifier is difficult to ensure the accuracy of classification. In order to address the above issues, we propose an abnormal event detection hybrid modulation method via feature expectation subgraph calibrating classification in video surveillance scenes in this paper. Our main contribution is to calibrate the classification of a single classifier by constructing feature expectation subgraphs. First, we employ convolutional neural network and long short-term memory models to extract the spatiotemporal features of video frame, and then construct the feature expectation subgraph for each key frame of every video, which could be used to capture the internal sequential and topological relational characteristics of structured feature vector. Second, we project expectation subgraphs on the sparse vector to combine with a support vector classifier to calibrate the results of a linear support vector classifier. Finally, the experiments on a common dataset named UCSDped1 and a coal mining video dataset in comparison with some existing works demonstrate that the performance of the proposed method is better than several the state-of-the-art approaches. INDEX TERMS Abnormal event detection, feature expectation subgraph, calibrating classification, sequential and topological relational characteristics.

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Section: A Hand-crafted Features-based Modelsmentioning

confidence: 99%

Abnormal Event Detection via Feature Expectation Subgraph Calibrating Classification in Video Surveillance Scenes

Deng

et al. 2020

IEEE Access

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“…Ristic et al [4] are using the abnormality detection as a means of preprocessing the data. In later works [2], [5], [6], we see that pre-processing the data is of great importance, especially when machine learning is used for the creation of the models, either alone or in cooperation with the clustering algorithms. That importance has led to g reat improvements in this domain over time, creating more customized solutions, according to the research problem we are trying to solve in each case, the tools we are using and the features we decided to work with.…”

Section: Related Work 31 Trajectory Predictionmentioning

confidence: 99%

“…Some of the latest works are using DBSCAN clustering on AIS datasets in order to identify normal trajectories of naval vessels or points of interest and locate abnormal or anomalous activity [5], [6]. DBSCAN is excellent in point of interest identification because it can be parameterized in order to isolate certain behaviors that point to specific types of hotspots.…”

Section: Related Work 31 Trajectory Predictionmentioning

confidence: 99%

Context agnostic trajectory prediction based on λ-architecture

Psomakelis

Tserpes

Zissis

et al. 2020

Future Generation Computer Systems

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Predicting the next position of movable objects has been a problem for at least the last three decades, referred to as 'trajectory prediction'. In our days, the vast amounts of data being continuously produced add the big data dimension to the trajectory prediction problem, which we are trying to tackle by creating a λ-Architecture based analytics platform. This platform performs both batch and stream analytics tasks and then combines them to perform analytical tasks that cannot be performed by analyzing any of these layers by itself. The biggest benefit of this platform is its context agnostic trait, which allows us to use it for any use case, as long as a time-stamped geo-location stream is provided. The experimental results presented prove that each part of the λ-Architecture performs well at certain targets, making a combination of these parts a necessity in order to improve the overall accuracy and performance of the platform.

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“…The authors of [6] proposed a framework named MT-MAD for maritime trajectory modeling and anomaly detection. The authors of [7] judged whether the ship's trajectory was abnormal based on type of ship, traffic direction, speed, course, number of stop points, and number of phase changes. The authors of [8] used trajectory kernels with one-class SVMs to find outlying trajectories.…”

Section: Introductionmentioning

confidence: 99%

Detection of AIS Closing Behavior and MMSI Spoofing Behavior of Ships Based on Spatiotemporal Data

et al. 2020

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In marine transportation, many ships are equipped with AIS devices. The AIS data sent by AIS devices can help the maritime authorities and other ships obtain the navigation condition of the ship, thereby ensuring the safety of ships during navigation. However, when a ship is involved in illegal activities, the crew may close the AIS device or tamper with Maritime Mobile Service Identity (MMSI) in the AIS data. To detect these two kinds of behaviors, this paper designs the AIS closing detection algorithm and MMSI spoofing detection algorithm based on the spatiotemporal data provided by AIS and radar. As the radar data does not include the ship’s identification, the associated relationship between radar data and AIS data is difficult to determine in the multi-ship scenario. To solve this problem, the D-TRAP is defined in this paper, it is applied in the process of searching for the associated AIS points of radar trajectory points, when the number of effective AIS points is reduced caused by the above two behaviors, the association method used in the paper has better performance. In addition, real data and simulation data are used to verify the two algorithms. The verification results show that when the ship is simultaneously monitored by radar and AIS, and the monitoring process continues for a period of time, the AIS closing detection algorithm has good performance. When the ship is monitored by AIS and radar before and after MMSI spoofing, and both monitoring processes continue for a period of time, the MMSI spoofing algorithm has good performance.

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Finding Abnormal Vessel Trajectories Using Feature Learning

Cited by 48 publications

References 29 publications

Abnormal Event Detection via Feature Expectation Subgraph Calibrating Classification in Video Surveillance Scenes

Abnormal Event Detection via Feature Expectation Subgraph Calibrating Classification in Video Surveillance Scenes

Context agnostic trajectory prediction based on λ-architecture

Detection of AIS Closing Behavior and MMSI Spoofing Behavior of Ships Based on Spatiotemporal Data

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