Trajectory extraction for abnormal behavior detection in public area

Lee, Jaejung; Kim, Gyu-Jin; Kim, Moon-Hyun

doi:10.1109/cewit.2012.6606979

Cited by 8 publications

(5 citation statements)

References 12 publications

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“…In this method, preprocessing of noise reduction, 5,15 background modeling, 27 morphological processing, 28 shadow elimination 29 were performed successively to obtain multiple targets. Pedestrian and vehicle were classified according to the length‐width ratio and the duty cycle of the target box 15 .…”

Section: Traffic Violation Recognitionmentioning

confidence: 99%

Recognition method of traffic violations based on complex interaction between multiple entities

Shen

Teng

2021

Int J of Intelligent Sys

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Existing methods used in detecting vehicles with traffic violations are mostly based on single‐entity frameworks, and those involving multientities remain to be limited. In this paper, we propose a traffic violation detection model, an intelligent vehicle violation recognition method based on multiple entities. It aims to identify vehicles violating the rule of yielding to pedestrians at nonsignalized crosswalks. First, we define the concepts of vehicles and pedestrians and then apply the regression background subtraction method and particle filter algorithm to automatically identify and track moving objects. Second, the pedestrian validity feature template is specified to detect temporal trajectory features from videos with labels and to train classification networks aiming to identify unreasonable behavior patterns, such as remaining on the sidewalk, entering the roadway, bicycle riding, and others. Finally, we develop a novel traffic violation recognition method based on multientity interaction analysis. The cases of failing to yield to pedestrians are recognized based on the multientity feature template built using the proposed method. We verified the effectiveness of the proposed method on a real traffic data set obtained from surveillance cameras. The obtained results are significantly better compared with the baseline method. The area under curve value of the proposed traffic violation recognition method with multientity interaction is 14.5%, 11.1%, and 6.6% higher compared with the three baseline methods based on single‐entity frameworks.

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Section: Traffic Violation Recognitionmentioning

confidence: 99%

Recognition method of traffic violations based on complex interaction between multiple entities

Shen

Teng

2021

Int J of Intelligent Sys

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“…Therefore, we introduce joint training as the second stage that aims to extract the most useful latent data representations, and at the same time, enhance discrimination ability. Besides, we apply a schedule of dynamic loss weights to control the degree of distortion of the underlying distribution, that is, we change the value of α, β, γ in (18) over training epochs (detailed in part B of next section). Note that during the training, we choose Adam optimizer as our optimization technique, and use Leaky Rectified Linear Unit (Leaky ReLU) as the activation function, which overcomes dying neuron issue of ReLU.…”

Section: E Model Trainingmentioning

confidence: 99%

“…Understanding and discovering knowledge from trajectory data is a key issue in urban computing [1]. In real life, numerous applications are based on trajectory data mining techniques such as effectively identifying users' transportation modes [2]- [9], analysis on vessel activities [10]- [13], traffic congestion prediction [14]- [16], stampede events warning [15], and abnormal trajectory detection [17], [18]. For research purposes, studies like learning animal migratory habits and identifying the intensity of hurricane [19] are highly relying on trajectory data mining techniques.…”

Section: Introductionmentioning

confidence: 99%

Dual Supervised Autoencoder Based Trajectory Classification Using Enhanced Spatio-Temporal Information

Xia

2020

IEEE Access

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With the rapid development of mobile internet and location awareness techniques, massive spatio-temporal data is collected every day. Trajectory classification is critically important to many realworld applications such as human mobility understanding, urban planning, and intelligent transportation systems. A growing number of studies took advantage of the deep learning method to learn the high-level features of trajectory data for accurate estimation. However, some of these studies didn't interpret spatiotemporal information well, more importantly, they didn't fully utilize the high-level features extracted by neural networks. To overcome these drawbacks, this paper utilizes the proposed stop state and turn state to enhance spatial information, and at the same time, extracts stronger time information via Recurrence Plot (RP). Moreover, a novel Dual Convolutional neural networks based Supervised Autoencoder (Dual-CSA) is proposed by making the network aware of Predefined Class Centroids (PCC). Experiments conducted on two real-world datasets demonstrate that Dual-CSA can learn the high-level features well. The highest accuracy of the Geolife and SHL datasets are 89.475% and 89.602%, respectively, proving the superiority of our method.

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“…Crowd control system should be able to provide early warning of the occurrence of abnormal behavior in the video environment. Motion trajectory could be obtained by using the moving human tracking algorithm and the abnormal behavior could be estimated by extracting the length, pixel, covariance, motion degree and other information from the trajectory [7].The study of behavioral analysis mainly focuses on the behavior identification of a limited class of simple rules or abnormal behavior detection of single target in a particular scene [8], complex and large amount of calculation of the image processing should be carried out before behavioral analysis, this reduces the efficiency of the algorithm and it's difficult to achieve intelligent frontend equipment. Most of the algorithms can only achieve the static matching of single target behavior in the process of analysis and the capture efficiency is low.…”

Section: Analysis Of the Behavior Of Abnormal Objectsmentioning

confidence: 99%

Based on Wide Area Environment Abnormal Behavior Analysis and Anomaly Detection Research

Zhang

2016

MATEC Web Conf.

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Abstract. Group anomaly identification and location is an important issue in the field of artificial intelligence. Capture of the accident source and rapid prediction of mass incidents in public places are difficult problems in intelligent video identification and processing, but the traditional group anomaly detection research has many limitations when it comes to accident source detection and intelligent recognition. We are to research on the algorithms of accident source location and abnormal group identification based on behavior analysis in the condition of dramatically changing group geometry appearance, including: 1) to propose a logic model of image density based on the social force model, and to build the crowd density trend prediction model integrating "fast and fuzzy matching at front-end" and "accurate and classified training at back-end"; 2) to design a fast abnormal source flagging algorithm based on support vector machine, and to realize intelligent and automatic marking of abnormal source point; 3) to construct a multi-view human body skeleton invariant moment model and a motion trajectory model based on linear parametric equations. The expected results of the research will help prevent abnormal events effectively, capture the first scene of incidents and the abnormal source point quickly, and play a decision support role in the proactive national security strategy.

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Trajectory extraction for abnormal behavior detection in public area

Cited by 8 publications

References 12 publications

Recognition method of traffic violations based on complex interaction between multiple entities

Recognition method of traffic violations based on complex interaction between multiple entities

Dual Supervised Autoencoder Based Trajectory Classification Using Enhanced Spatio-Temporal Information

Based on Wide Area Environment Abnormal Behavior Analysis and Anomaly Detection Research

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