2,731 research outputs found
Texture-based crowd detection and localisation
This paper presents a crowd detection system based on texture analysis. The state-of-the-art techniques based on co-occurrence matrix have been revisited and a novel set of features proposed. These features provide a richer description of the co-occurrence matrix, and can be exploited to obtain stronger classification results, especially when smaller portions of the image are considered. This is extremely useful for crowd localisation: acquired images are divided into smaller regions in order to perform a classification on each one. A thorough evaluation of the proposed system on a real world data set is also presented: this validates the improvements in reliability of the crowd detection and localisation
Socially Constrained Structural Learning for Groups Detection in Crowd
Modern crowd theories agree that collective behavior is the result of the
underlying interactions among small groups of individuals. In this work, we
propose a novel algorithm for detecting social groups in crowds by means of a
Correlation Clustering procedure on people trajectories. The affinity between
crowd members is learned through an online formulation of the Structural SVM
framework and a set of specifically designed features characterizing both their
physical and social identity, inspired by Proxemic theory, Granger causality,
DTW and Heat-maps. To adhere to sociological observations, we introduce a loss
function (G-MITRE) able to deal with the complexity of evaluating group
detection performances. We show our algorithm achieves state-of-the-art results
when relying on both ground truth trajectories and tracklets previously
extracted by available detector/tracker systems
Detection and Simulation of Dangerous Human Crowd Behavior
Tragically, gatherings of large human crowds quite often end in crowd disasters such as the recent catastrophe at the Loveparade 2010. In the past, research on pedestrian and crowd dynamics focused on simulation of pedestrian motion. As of yet, however, there does not exist any automatic system which can detect hazardous situations in crowds, thus helping to prevent these tragic incidents. In the thesis at hand, we analyze pedestrian behavior in large crowds and observe characteristic motion patterns. Based on our findings, we present a computer vision system that detects unusual events and critical situations from video streams and thus alarms security personnel in order to take necessary actions. We evaluate the system’s performance on synthetic, experimental as well as on real-world data. In particular, we show its effectiveness on the surveillance videos recorded at the Loveparade crowd stampede. Since our method is based on optical flow computations, it meets two crucial prerequisites in video surveillance: Firstly, it works in real-time and, secondly, the privacy of the people being monitored is preserved. In addition to that, we integrate the observed motion patterns into models for simulating pedestrian motion and show that the proposed simulation model produces realistic trajectories. We employ this model to simulate large human crowds and use techniques from computer graphics to render synthetic videos for further evaluation of our automatic video surveillance system
A cloud-based deep learning system for improving crowd safety at event entrances
Crowding at the entrances of large events may lead to critical and
life-threatening situations, particularly when people start pushing each other
to reach the event faster. A system for automatic and timely identification of
pushing behavior would help organizers and security forces to intervene early
and mitigate dangerous situations. In this paper, we propose a cloud-based deep
learning system for early detection of pushing automatically in the live video
stream of crowded event entrances. The proposed system relies mainly on two
models: a pre-trained deep optical flow and an adapted version of the
EfficientNetV2B0 classifier. The optical flow model extracts the
characteristics of the crowd motion in the live video stream, while the
classifier analyses the crowd motion and annotates pushing patches in the live
stream. A novel dataset is generated based on five real-world experiments and
their associated ground truth data to train the adapted EfficientNetV2B0 model.
The experimental situations simulated a crowded event entrance, and social
psychologists manually created the ground truths for each video experiment.
Several experiments on the videos and the generated dataset are carried out to
evaluate the accuracy and annotation delay time of the proposed system.
Furthermore, the experts manually revised the annotation results of the system.
Findings indicate that the system identified pushing behaviors with an accuracy
rate of 89% within an acceptable delay time.Comment: 15 pages, 8 figures, 4 tables, journa
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