5,343 research outputs found
Online real-time crowd behavior detection in video sequences
Automatically detecting events in crowded scenes is a challenging task in Computer Vision. A number of offline approaches have been proposed for solving the problem of crowd behavior detection, however the offline assumption limits their application in real-world video surveillance systems. In this paper, we propose an online and real-time method for detecting events in crowded video sequences. The proposed approach is based on the combination of visual feature extraction and image segmentation and it works without the need of a training phase. A quantitative experimental evaluation has been carried out on multiple publicly available video sequences, containing data from various crowd scenarios and different types of events, to demonstrate the effectiveness of the approach
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
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
DRL-VO: Learning to Navigate Through Crowded Dynamic Scenes Using Velocity Obstacles
This paper proposes a novel learning-based control policy with strong
generalizability to new environments that enables a mobile robot to navigate
autonomously through spaces filled with both static obstacles and dense crowds
of pedestrians. The policy uses a unique combination of input data to generate
the desired steering angle and forward velocity: a short history of lidar data,
kinematic data about nearby pedestrians, and a sub-goal point. The policy is
trained in a reinforcement learning setting using a reward function that
contains a novel term based on velocity obstacles to guide the robot to
actively avoid pedestrians and move towards the goal. Through a series of 3D
simulated experiments with up to 55 pedestrians, this control policy is able to
achieve a better balance between collision avoidance and speed (i.e., higher
success rate and faster average speed) than state-of-the-art model-based and
learning-based policies, and it also generalizes better to different crowd
sizes and unseen environments. An extensive series of hardware experiments
demonstrate the ability of this policy to directly work in different real-world
environments with different crowd sizes with zero retraining. Furthermore, a
series of simulated and hardware experiments show that the control policy also
works in highly constrained static environments on a different robot platform
without any additional training. Lastly, several important lessons that can be
applied to other robot learning systems are summarized. Multimedia
demonstrations are available at
https://www.youtube.com/watch?v=KneELRT8GzU&list=PLouWbAcP4zIvPgaARrV223lf2eiSR-eSS.Comment: Accepted by IEEE Transactions on Robotics (T-RO), 202
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