Scalable Detection of Crowd Motion Patterns

Heldens, Stijn; Litvak, Nelly; Steen, Maarten van

doi:10.1109/tkde.2018.2879079

Cited by 7 publications

(2 citation statements)

References 36 publications

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“…For purposes like surveillance, a group of methods known as background subtraction can be used to isolate items of interest from a scene. Calculating a reference image, deducting each new frame from it, and thresholding the outcome are all steps in the background removal process [45].…”

Section: Crowd Motion Detectionmentioning

confidence: 99%

Machine Learning-Based Crowd behavior Analysis and Forecasting

Bhardwaj

Dwivedi

Pandey³

et al. 2023

IJSRCSEIT

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In many places today, the world's overcrowding causes crowded conditions. Analysis of crowd activity is a developing field of study. It is common knowledge that mob activity can forecast what might happen during an event. Crowd management could be very effective if situations like riots, mass lynchings, traffic jams, accidents, stampedes, etc. could be predicted beforehand. In this paper, we propose a new multicolumn convolutional neural network (MCNN) based technique for predicting mob behavior. The features of the incoming image are first analyzed and extracted. The approximated number of the gathering is then established, and image cropping is completed. For each area of the image, low level characteristics are retrieved. The objects in the picture are then created as density images. Using our method, the gathered characteristics and their object density maps are then linearly mapped. At last, we forecast and quantify the population using the MCNN algorithm. For the ShanghaiTech dataset, we have evaluated our method using actual data.

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Section: Crowd Motion Detectionmentioning

confidence: 99%

Machine Learning-Based Crowd behavior Analysis and Forecasting

Bhardwaj

Dwivedi

Pandey³

et al. 2023

IJSRCSEIT

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show abstract

“…without considering directional and time information. With a tilted trigonometric bell [13], the density of dynamic points can be acquired by incorporating information of time and position changes of points. In addition, the kernel's bandwidth is determined by the points at time ti and ti+1.…”

Section: Spatio-temporal Tracklet Extractionmentioning

confidence: 99%

Vessel Crowd Movement Pattern Mining for Maritime Traffic Management

Wen

Yan

2019

LOGI – Scientific Journal on Transport and Logistics

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The goal of maritime traffic management is to provide a safe and efficient maritime environment for different type of vessels facilitating port logistics and supply chain business. However, current maritime traffic management mainly relies on the massive individual vessel’s data for decision making. Lack of macro-level understanding of vessel crowd movement around port challenges maritime safety and traffic efficiency. In this paper, we describe a spatio-temporal data mining method to discover crowd movement patterns of vessels from their short-term history data. The method first captures vessels’ crowd movement features by building vessels’ tracklets with their speed and location. A movement vector clustering algorithm is developed to find different travel behaviors for different group of vessels. With nonparametric regression on the classified vessel movement vectors which represent the crowd travel behaviors, an overall vessel movement pattern can then be discovered. In this research, we tested real trajectory data of vessels near Singapore ports. Comparing with the actual massive vessel movement data, we found that this method was able to extract vessels’ crowd movement information. The hotspots on risk area in terms of vessel traffic and speed can be identified. The method can be used to provide decision-making support for maritime traffic management.

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A survey on abnormal detection in video surveillances

Manikandan,

Rahamathunnisa

2024

AIP Conference Proceedings

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Scalable Detection of Crowd Motion Patterns

Cited by 7 publications

References 36 publications

Machine Learning-Based Crowd behavior Analysis and Forecasting

Machine Learning-Based Crowd behavior Analysis and Forecasting

Vessel Crowd Movement Pattern Mining for Maritime Traffic Management

A survey on abnormal detection in video surveillances

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