1,721 research outputs found
LCrowdV: Generating Labeled Videos for Simulation-based Crowd Behavior Learning
We present a novel procedural framework to generate an arbitrary number of
labeled crowd videos (LCrowdV). The resulting crowd video datasets are used to
design accurate algorithms or training models for crowded scene understanding.
Our overall approach is composed of two components: a procedural simulation
framework for generating crowd movements and behaviors, and a procedural
rendering framework to generate different videos or images. Each video or image
is automatically labeled based on the environment, number of pedestrians,
density, behavior, flow, lighting conditions, viewpoint, noise, etc.
Furthermore, we can increase the realism by combining synthetically-generated
behaviors with real-world background videos. We demonstrate the benefits of
LCrowdV over prior lableled crowd datasets by improving the accuracy of
pedestrian detection and crowd behavior classification algorithms. LCrowdV
would be released on the WWW
Crowd detection and counting using a static and dynamic platform: state of the art
Automated object detection and crowd density estimation are popular and important area in visual surveillance research. The last decades witnessed many significant research in this field however, it is still a challenging problem for automatic visual surveillance. The ever increase in research of the field of crowd dynamics and crowd motion necessitates a detailed and updated survey of different techniques and trends in this field. This paper presents a survey on crowd detection and crowd density estimation from moving platform and surveys the different methods employed for this purpose. This review category and delineates several detections and counting estimation methods that have been applied for the examination of scenes from static and moving platforms
Human detection in surveillance videos and its applications - a review
Detecting human beings accurately in a visual surveillance system is crucial for diverse application areas including abnormal event detection, human gait characterization, congestion analysis, person identification, gender classification and fall detection for elderly people. The first step of the detection process is to detect an object which is in motion. Object detection could be performed using background subtraction, optical flow and spatio-temporal filtering techniques. Once detected, a moving object could be classified as a human being using shape-based, texture-based or motion-based features. A comprehensive review with comparisons on available techniques for detecting human beings in surveillance videos is presented in this paper. The characteristics of few benchmark datasets as well as the future research directions on human detection have also been discussed
A Survey on Counting People with Low Level Features
The main objective of this paper is to evaluate recent development in counting people with low level features. This paper describe the various techniques of counting people with low level features, compares them with the help of evaluation performance measures which are widely used for counting. The aim of this paper is to find the best method among some prominent exiting methods
Monitoring social distancing with single image depth estimation
The recent pandemic emergency raised many challenges regarding the
countermeasures aimed at containing the virus spread, and constraining the
minimum distance between people resulted in one of the most effective
strategies. Thus, the implementation of autonomous systems capable of
monitoring the so-called social distance gained much interest. In this paper,
we aim to address this task leveraging a single RGB frame without additional
depth sensors. In contrast to existing single-image alternatives failing when
ground localization is not available, we rely on single image depth estimation
to perceive the 3D structure of the observed scene and estimate the distance
between people. During the setup phase, a straightforward calibration
procedure, leveraging a scale-aware SLAM algorithm available even on consumer
smartphones, allows us to address the scale ambiguity affecting single image
depth estimation. We validate our approach through indoor and outdoor images
employing a calibrated LiDAR + RGB camera asset. Experimental results highlight
that our proposal enables sufficiently reliable estimation of the
inter-personal distance to monitor social distancing effectively. This fact
confirms that despite its intrinsic ambiguity, if appropriately driven single
image depth estimation can be a viable alternative to other depth perception
techniques, more expensive and not always feasible in practical applications.
Our evaluation also highlights that our framework can run reasonably fast and
comparably to competitors, even on pure CPU systems. Moreover, its practical
deployment on low-power systems is around the corner.Comment: Accepted for pubblication on IEEE Transactions on Emerging Topics in
Computational Intelligence (TETCI
Large scale monitoring of crowds and building utilisation: A new database and distributed approach
Public buildings and large infrastructure are typically monitored by tens or hundreds of cameras, all capturing different physical spaces and observing different types of interactions and behaviours. However to date, in large part due to limited data availability, crowd monitoring and operational surveillance research has focused on single camera scenarios which are not representative of real-world applications. In this paper we present a new, publicly available database for large scale crowd surveillance. Footage from 12 cameras for a full work day covering the main floor of a busy university campus building, including an internal and external foyer, elevator foyers, and the main external approach are provided; alongside annotation for crowd counting (single or multi-camera) and pedestrian flow analysis for 10 and 6 sites respectively. We describe how this large dataset can be used to perform distributed monitoring of building utilisation, and demonstrate the potential of this dataset to understand and learn the relationship between different areas of a building
AGORASET: a dataset for crowd video analysis
International audienceThe ability of efficient computer vision tools (detec- tion of pedestrians, tracking, ...) as well as advanced rendering techniques have enabled both the analysis of crowd phenomena and the simulation of realistic sce- narios. A recurrent problem lies in the evaluation of those methods since few common benchmark are avail- able to compare and evaluate the techniques is avail- able. This paper proposes a dataset of crowd sequences with associated ground truths (individual trajectories of each pedestrians inside the crowd, related continuous quantities of the scene such as the density and the veloc- ity field). We chosed to rely on realistic image synthesis to achieve our goal. As contributions of this paper, a typology of sequences relevant to the computer vision analysis problem is proposed, along with images of se- quences available in the database
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