A Balcony Dangerous Behavior Detection Algorithm Based on Video Image

2019 ◽  
Author(s):  
Rongchun Sun ◽  
Xuemei Song ◽  
Yue Tang ◽  
Jian Wang
Keyword(s):  
Detection Algorithm ◽  
Video Image ◽  
Behavior Detection ◽  
Dangerous Behavior

Driver Behavior Detection Based on Multi-GNSS Precise Point Positioning Technology

2014 ◽  
Vol 619 ◽  
pp. 327-331 ◽  
Author(s):  
Nuksit Noomwongs ◽  
Raksit Thitipatanapong ◽  
Sunhapos Chantranuwathana ◽  
Sanya Klongnaivai
Keyword(s):  
Navigation System ◽  
Precise Point Positioning ◽  
Driver Behavior ◽  
Scale Up ◽  
Detection Algorithm ◽  
Base Station ◽  
Lateral Acceleration ◽  
Navigation Satellite ◽  
Behavior Detection ◽  
Point Positioning

Driver behavior is the key to safe mobility. In general, vehicle maneuvers can be determined from acceleration of the vehicle. Physically, the acceleration and brake can be detected with longitudinal acceleration while turning and lane change can be detected with lateral acceleration. Nowadays, navigation system technologies have been much improved both on availability and accuracy with combination of multiple navigation satellite systems. Normally, it’s called Multi-GNSS (multiple global navigation satellite system). With decimeter precision and the update rate scale up to 10-Hz, the GNSS could be an alternative solution for driver behavior detection. In this paper, advance Multi-GNSS with precise point position (PPP) technique was presented with a simple maneuver detection algorithm. The advantage of PPP over conventional navigation is decimeter accuracy without direct connection to any reference base station. The experimental Multi-GNSS receiver was JAVAD Delta G3T that installed on a utility vehicle. This high performance multi-GNSS navigation system was investigated with the driving behavior detection algorithm. The precise point positioning (PPP) technique in combination with multiple satellite navigation system (GPS+GLONASS+GALOLEO+QZSS) were applied in this study. The PPP technique improved the output of detection algorithm in acceleration limit from 260% error in conventional navigation system (GPS) to 20% of incidents with PPP.

An Abnormal Crowd Behavior Detection Algorithm Based on Fluid Mechanics

2014 ◽  
Vol 9 (5) ◽  
Author(s):  
Xiaofei Wang ◽  
Mingliang Gao ◽  
Xiaohai He ◽  
Xiaohong Wu ◽  
Yun Li
Keyword(s):  
Fluid Mechanics ◽  
Detection Algorithm ◽  
Crowd Behavior ◽  
Behavior Detection

scholarly journals Abnormal Ship Behavior Detection after the Closure of AIS Based on Radar Data

2021 ◽  
Author(s):  
Tao Zhang ◽  
Chuanchang Liu ◽  
Bodong Wen
Keyword(s):  
Reporting System ◽  
Radar Data ◽  
Detection Algorithm ◽  
Trajectory Data ◽  
Behavior Detection ◽  
The Earth ◽  
Illegal Activities ◽  
Abnormal Behaviors ◽  
Influence Of Noise ◽  
Ship Behavior

Abstract In marine transportation, most ships are equipped with AIS devices. The AIS data sent by these devices can help maritime authorities to manage ships in relevant sea areas. However, AIS is a self-reporting system, when a ship is engaged in illegal activities, the AIS device may be turned off. Therefore, after the AIS is closed, if the ship's behavior during a certain period of time is different from the ship's behavior before the closure of AIS, the different behavior is likely to represent that the ship is conducting illegal activities. This behavior is considered abnormal and needs to be detected in time. Based on radar trajectory data, the detection of abnormal ship behavior is studied from two aspects: speed and direction. In order to improve the intelligent level of abnormal ship behavior detection, the abnormal speed behavior detection algorithm combined with rules and clustering (ASBD-RC) and the abnormal direction behavior detection algorithm combined with partition and the earth mover's distance (ADBD-PE) are proposed. The ASBD-RC algorithm can reduce the influence of noise and sea clutter on abnormal speed behavior detection. The ADBD-PE algorithm can effectively partition and identify trajectory segments with abnormal direction. In the experiment, based on the real and simulated radar trajectories, the abnormal behaviors of ships under different scenarios are generated. The experimental results show that in most scenarios, the ASBD-RC algorithm and the ADBD-PE algorithm can effectively detect abnormal ship behavior. And compared with other algorithms, the proposed two algorithms have better and more stable detection results.

Incremental learning of an abnormal behavior detection algorithm based on principal components

2020 ◽  
Vol 44 (3) ◽  
pp. 476-481
Author(s):  
R.A. Shatalin ◽  
V.R. Fidelman ◽  
P.E. Ovchinnikov
Keyword(s):  
Principal Components ◽  
Incremental Learning ◽  
Principal Component ◽  
Detection Algorithm ◽  
Error Rates ◽  
Abnormal Behavior ◽  
Learning Time ◽  
Behavior Detection ◽  
Training Samples ◽  
Abnormal Behavior Detection

In this paper, we propose an incremental learning scheme for the abnormal behavior detection algorithm based on principal component. The results obtained on a UCSD dataset and our experimental videos at a different number of training samples show that error rates are similar to conventional learning. Moreover, the proposed scheme allows the incremental learning time to be significantly reduced in comparison with a method based on matrix eigendecomposition.

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