A Cloud-based Deep Learning Framework for Early Detection of Pushing at
Crowded Event Entrances
- URL: http://arxiv.org/abs/2302.08237v2
- Date: Fri, 9 Jun 2023 19:45:42 GMT
- Title: A Cloud-based Deep Learning Framework for Early Detection of Pushing at
Crowded Event Entrances
- Authors: Ahmed Alia, Mohammed Maree, Mohcine Chraibi, Anas Toma and Armin
Seyfried
- Abstract summary: We propose a cloud-based deep learning framework for automatic early detection of pushing in crowded event entrances.
A novel dataset is generated based on five real-world experiments and their associated ground truth data.
The proposed framework identified pushing behaviors with an accuracy rate of 87% within a reasonable delay time.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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. 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 framework for automatic early detection of pushing in crowded event
entrances. The proposed framework initially modifies and trains the
EfficientNetV2B0 Convolutional Neural Network model. Subsequently, it
integrates the adapted model with an accurate and fast pre-trained deep optical
flow model with the color wheel method to analyze video streams and identify
pushing patches in real-time. Moreover, the framework uses live capturing
technology and a cloud-based environment to collect video streams of crowds in
real-time and provide early-stage results. 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 setups simulated a crowded
event entrance, while the ground truths for each video experiment was generated
manually by social psychologists. Several experiments on the videos and the
generated dataset are carried out to evaluate the accuracy and annotation delay
time of the proposed framework. The experimental results show that the proposed
framework identified pushing behaviors with an accuracy rate of 87% within a
reasonable delay time.
Related papers
- Predicting Long-horizon Futures by Conditioning on Geometry and Time [49.86180975196375]
We explore the task of generating future sensor observations conditioned on the past.
We leverage the large-scale pretraining of image diffusion models which can handle multi-modality.
We create a benchmark for video prediction on a diverse set of videos spanning indoor and outdoor scenes.
arXiv Detail & Related papers (2024-04-17T16:56:31Z) - Genuine Knowledge from Practice: Diffusion Test-Time Adaptation for
Video Adverse Weather Removal [53.15046196592023]
We introduce test-time adaptation into adverse weather removal in videos.
We propose the first framework that integrates test-time adaptation into the iterative diffusion reverse process.
arXiv Detail & Related papers (2024-03-12T14:21:30Z) - An Adaptive Framework for Generalizing Network Traffic Prediction
towards Uncertain Environments [51.99765487172328]
We have developed a new framework using time-series analysis for dynamically assigning mobile network traffic prediction models.
Our framework employs learned behaviors, outperforming any single model with over a 50% improvement relative to current studies.
arXiv Detail & Related papers (2023-11-30T18:58:38Z) - Event-based Vision for Early Prediction of Manipulation Actions [0.7699714865575189]
Neuromorphic visual sensors are artificial retinas that sequences output of events when brightness changes occur in the scene.
In this study, we introduce an event-based dataset on fine-grained manipulation actions.
We also perform an experimental study on the use of transformers for action prediction with events.
arXiv Detail & Related papers (2023-07-26T17:50:17Z) - StreamYOLO: Real-time Object Detection for Streaming Perception [84.2559631820007]
We endow the models with the capacity of predicting the future, significantly improving the results for streaming perception.
We consider multiple velocities driving scene and propose Velocity-awared streaming AP (VsAP) to jointly evaluate the accuracy.
Our simple method achieves the state-of-the-art performance on Argoverse-HD dataset and improves the sAP and VsAP by 4.7% and 8.2% respectively.
arXiv Detail & Related papers (2022-07-21T12:03:02Z) - Self-SuperFlow: Self-supervised Scene Flow Prediction in Stereo
Sequences [12.650574326251023]
In this paper, we explore the extension of a self-supervised loss for scene flow prediction.
Regarding the KITTI scene flow benchmark, our method outperforms the corresponding supervised pre-training of the same network.
arXiv Detail & Related papers (2022-06-30T13:55:17Z) - Real-time Object Detection for Streaming Perception [84.2559631820007]
Streaming perception is proposed to jointly evaluate the latency and accuracy into a single metric for video online perception.
We build a simple and effective framework for streaming perception.
Our method achieves competitive performance on Argoverse-HD dataset and improves the AP by 4.9% compared to the strong baseline.
arXiv Detail & Related papers (2022-03-23T11:33:27Z) - Learning Monocular Dense Depth from Events [53.078665310545745]
Event cameras produce brightness changes in the form of a stream of asynchronous events instead of intensity frames.
Recent learning-based approaches have been applied to event-based data, such as monocular depth prediction.
We propose a recurrent architecture to solve this task and show significant improvement over standard feed-forward methods.
arXiv Detail & Related papers (2020-10-16T12:36:23Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.