scholarly journals A Multitask Cascading CNN with MultiScale Infrared Optical Flow Feature Fusion-Based Abnormal Crowd Behavior Monitoring UAV

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5550
Author(s):  
Yanhua Shao ◽  
Wenfeng Li ◽  
Hongyu Chu ◽  
Zhiyuan Chang ◽  
Xiaoqiang Zhang ◽  
...  
Keyword(s):  
Optical Flow ◽  
Feature Fusion ◽  
Vision System ◽  
Abnormal Behavior ◽  
Single Type ◽  
Ir Camera ◽  
Average Speed ◽  
Crowd Density ◽  
Public Areas ◽  
Crowd Behaviors

Visual-based object detection and understanding is an important problem in computer vision and signal processing. Due to their advantages of high mobility and easy deployment, unmanned aerial vehicles (UAV) have become a flexible monitoring platform in recent years. However, visible-light-based methods are often greatly influenced by the environment. As a result, a single type of feature derived from aerial monitoring videos is often insufficient to characterize variations among different abnormal crowd behaviors. To address this, we propose combining two types of features to better represent behavior, namely, multitask cascading CNN (MC-CNN) and multiscale infrared optical flow (MIR-OF), capturing both crowd density and average speed and the appearances of the crowd behaviors, respectively. First, an infrared (IR) camera and Nvidia Jetson TX1 were chosen as an infrared vision system. Since there are no published infrared-based aerial abnormal-behavior datasets, we provide a new infrared aerial dataset named the IR-flying dataset, which includes sample pictures and videos in different scenes of public areas. Second, MC-CNN was used to estimate the crowd density. Third, MIR-OF was designed to characterize the average speed of crowd. Finally, considering two typical abnormal crowd behaviors of crowd aggregating and crowd escaping, the experimental results show that the monitoring UAV system can detect abnormal crowd behaviors in public areas effectively.

A Classification Method Based on Multi-Features for Abnormal Crowd Behaviors

2014 ◽  
Vol 685 ◽  
pp. 501-506
Author(s):  
Xiao Fei Wang ◽  
Jun Hu ◽  
Yin Kuang ◽  
Wei Yu ◽  
Rui Sun
Keyword(s):  
Density Estimation ◽  
Experimental Results ◽  
Classification Method ◽  
Abnormal Behavior ◽  
Public Security ◽  
Crowd Behavior ◽  
Crowd Density Estimation ◽  
Crowd Density ◽  
The Social ◽  
Crowd Behaviors

An abnormal crowd behavior would harm social public security. Different abnormal crowd behaviors would bring about different harms and subject to different attentions of social public. The higher the harm of the abnormal crowd behavior was, the higher the attention of the social public would be. Therefore, in this paper, a classification method, using crowd density estimation and crowd intensity, is proposed for abnormal crowd behaviors. In order to describe the abnormal crowd behavior more reasonably, we shoot some videos. Finally, the effectiveness and accuracy of our method for abnormal behavior classification is validated by the experimental results.

scholarly journals Crowd Counting and Abnormal Behavior Detection via Multiscale GAN Network Combined with Deep Optical Flow

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Beibei Song ◽  
Rui Sheng
Keyword(s):  
Optical Flow ◽  
Abnormal Behavior ◽  
Feature Descriptor ◽  
Single Image ◽  
Crowd Counting ◽  
Behavior Detection ◽  
Crowd Density ◽  
Abnormal Behavior Detection ◽  
Detection And Counting ◽  
Low Performance

Aiming at the problem of low performance of crowd abnormal behavior detection caused by complex backgrounds and occlusions, this paper proposes a single-image crowd counting and abnormal behavior detection via multiscale GAN network. The proposed method firstly designed an embedded GAN module with a multibranch generator and a regional discriminator to initially generate crowd-density maps; and then our proposed multiscale GAN module is added to further strengthen the generalization ability of the model, which can effectively improve the accuracy and robustness of the prediction detection and counting. On the basis of single-image crowd counting, synthetic optical-flow feature descriptor is adopted to obtain the crowd motion trajectory, and the classification of abnormal behavior is finally implemented. The simulation results show that the proposed algorithm can significantly improve the accuracy and robustness of crowd counting and abnormal behavior detection in real complex scenarios compared with the existing mainstream algorithms, which is suitable for engineering applications.

The Analysis of Urban Road Traffic State Based on Kinds Floating Car Data

2014 ◽  
Vol 694 ◽  
pp. 80-84
Author(s):  
Xiao Tong Yin ◽  
Chao Qun Ma ◽  
Liang Peng Qu
Keyword(s):  
Road Traffic ◽  
Velocity Estimation ◽  
Least Square ◽  
Single Type ◽  
Estimation Model ◽  
Average Speed ◽  
Urban Road ◽  
Traffic State ◽  
Floating Car Data ◽  
Different Types

The analysis of the unban road traffic state based on kinds of floating car data, is based on the model and algorithm of floating car data preprocessing and map matching, etc. Firstly, according to the characteristics of the different types of urban road, the urban road section division has been carried on the elaboration and optimization. And this paper introduces the method of calculating the section average speed with single floating car data, also applies the dynamic consolidation of sections to estimate the section average velocity.Then the minimum sample size of floating car data is studied, and section average velocity estimation model based on single type of floating car data in the different case of floating car data sample sizes has been built. Finally, the section average speed of floating car in different types is fitted to the section average car speed by the least square method, using section average speed as the judgment standard, the grade division standard of urban road traffic state is established to obtain the information of road traffic state.

scholarly journals Vehicle Trajectory Estimation Based on Fusion of Visual Motion Features and Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7969
Author(s):  
Lianen Qu ◽  
Matthew N. Dailey
Keyword(s):  
Neural Networks ◽  
Optical Flow ◽  
Convolutional Neural Networks ◽  
Situation Awareness ◽  
Visual Motion ◽  
Accurate Method ◽  
Estimation Methods ◽  
Single Type ◽  
Global Localization ◽  
Trajectory Estimation

Driver situation awareness is critical for safety. In this paper, we propose a fast, accurate method for obtaining real-time situation awareness using a single type of sensor: monocular cameras. The system tracks the host vehicle’s trajectory using sparse optical flow and tracks vehicles in the surrounding environment using convolutional neural networks. Optical flow is used to measure the linear and angular velocity of the host vehicle. The convolutional neural networks are used to measure target vehicles’ positions relative to the host vehicle using image-based detections. Finally, the system fuses host and target vehicle trajectories in the world coordinate system using the velocity of the host vehicle and the target vehicles’ relative positions with the aid of an Extended Kalman Filter (EKF). We implement and test our model quantitatively in simulation and qualitatively on real-world test video. The results show that the algorithm is superior to state-of-the-art sequential state estimation methods such as visual SLAM in performing accurate global localization and trajectory estimation for host and target vehicles.

scholarly journals Optical Flow Approach Followed by SVM Classification Model to Recognize Abnormal Behavior of a Crowd

2018 ◽  
Vol 7 (3.34) ◽  
pp. 156
Author(s):  
Basavaraj G.M ◽  
Dr Ashok Kusagur
Keyword(s):  
Optical Flow ◽  
Promising Result ◽  
Classification Model ◽  
Abnormal Behavior ◽  
Support Vector ◽  
Force Model ◽  
Feature Points ◽  
Crowd Behavior ◽  
Operating Characteristics ◽  
Harris Corner

A many of researches have been carried out in the field of the crowd behavior recognition system. Recognizing crowd behavior in videos is most challenging and occlusions because of irregular human movement. This paper gives an overview of optical flow model along with the SVM (Support Vector Machine) classification model. This proposed approach evaluates sudden changes in motion of an event and classifies that event to a category: Normal and Abnormal.  Geometric means of location, direction, and displacement of the feature points of each frame are estimated. Harris corner Detector is used in each frame for tracking a set of feature points. Proposed approach is very effective in real time scenario like public places where security is most important. After analyzing result ROC curve (receiver operating characteristics) is plotted which gives classification accuracy. We also presented frame level comparison with Ground truth and social force model (SFM) techniques. Our proposed approach is giving a promising result compare to all state of art methods.  

Robot Vision System for Human Detection and Action Recognition

2020 ◽  
Vol 24 (3) ◽  
pp. 346-356
Author(s):  
Satoshi Hoshino ◽  
◽  
Kyohei Niimura
Keyword(s):  
Optical Flow ◽  
Real Time ◽  
Action Recognition ◽  
Autonomous Navigation ◽  
Vision System ◽  
Robot Vision ◽  
Human Detection ◽  
Time Performance ◽  
Camera Sensors ◽  
Camera Sensor

Mobile robots equipped with camera sensors are required to perceive humans and their actions for safe autonomous navigation. For simultaneous human detection and action recognition, the real-time performance of the robot vision is an important issue. In this paper, we propose a robot vision system in which original images captured by a camera sensor are described by the optical flow. These images are then used as inputs for the human and action classifications. For the image inputs, two classifiers based on convolutional neural networks are developed. Moreover, we describe a novel detector (a local search window) for clipping partial images around the target human from the original image. Since the camera sensor moves together with the robot, the camera movement has an influence on the calculation of optical flow in the image, which we address by further modifying the optical flow for changes caused by the camera movement. Through the experiments, we show that the robot vision system can detect humans and recognize the action in real time. Furthermore, we show that a moving robot can achieve human detection and action recognition by modifying the optical flow.

Sign in / Sign up

Export Citation Format

Share Document