An Approach to Event Recognition for Visual Surveillance Systems

2008 ◽  
Author(s):  
Yaohuan Cui ◽  
Changwoo Lee
Keyword(s):  
Visual Surveillance ◽  
Event Recognition ◽  
Surveillance Systems

scholarly journals Olympic Games Event Recognition via Transfer Learning with Photobombing Guided Data Augmentation

2021 ◽  
Vol 7 (2) ◽  
pp. 12
Author(s):  
Yousef I. Mohamad ◽  
Samah S. Baraheem ◽  
Tam V. Nguyen
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Data Augmentation ◽  
Olympic Games ◽  
Event Recognition ◽  
Surveillance Systems ◽  
Video Captioning ◽  
Practical Applications ◽  
Sport Events ◽  
The Olympic Games

Automatic event recognition in sports photos is both an interesting and valuable research topic in the field of computer vision and deep learning. With the rapid increase and the explosive spread of data, which is being captured momentarily, the need for fast and precise access to the right information has become a challenging task with considerable importance for multiple practical applications, i.e., sports image and video search, sport data analysis, healthcare monitoring applications, monitoring and surveillance systems for indoor and outdoor activities, and video captioning. In this paper, we evaluate different deep learning models in recognizing and interpreting the sport events in the Olympic Games. To this end, we collect a dataset dubbed Olympic Games Event Image Dataset (OGED) including 10 different sport events scheduled for the Olympic Games Tokyo 2020. Then, the transfer learning is applied on three popular deep convolutional neural network architectures, namely, AlexNet, VGG-16 and ResNet-50 along with various data augmentation methods. Extensive experiments show that ResNet-50 with the proposed photobombing guided data augmentation achieves 90% in terms of accuracy.

Learning Algorithms for Anomaly Detection from Images

Biometrics ◽  
2017 ◽  
pp. 281-308
Author(s):  
Tarem Ahmed ◽  
Al-Sakib Khan Pathan ◽  
Supriyo Shafkat Ahmed
Keyword(s):  
Human Security ◽  
Visual Surveillance ◽  
Automated System ◽  
Surveillance Systems ◽  
Human Beings ◽  
Security Officer ◽  
Security Officers ◽  
Large Numbers ◽  
Quantitative Performance ◽  
Present World

Visual surveillance networks are installed in many sensitive places in the present world. Human security officers are required to continuously stare at large numbers of monitors simultaneously, and for lengths of time at a stretch. Constant alert vigilance for hours on end is difficult to maintain for human beings. It is thus important to remove the onus of detecting unwanted activity from the human security officer to an automated system. While many researchers have proposed solutions to this problem in the recent past, significant gaps remain in existing knowledge. Most existing algorithms involve high complexities. No quantitative performance analysis is provided by most researchers. Most commercial systems require expensive equipment. This work proposes algorithms where the complexities are independent of time, making the algorithms naturally suited to online use. In addition, the proposed methods have been shown to work with the simplest surveillance systems that may already be publicly deployed. Furthermore, direct quantitative performance comparisons are provided.

Collective Event Detection by a Distributed Low-Cost Smart Camera Network

2019 ◽  
pp. 918-937
Author(s):  
Jhih-Yuan Hwang ◽  
Wei-Po Lee
Keyword(s):  
Event Detection ◽  
Low Cost ◽  
Event Recognition ◽  
Surveillance Systems ◽  
Collective Decision Making ◽  
Video Streams ◽  
Smart Cameras ◽  
Encoding Scheme ◽  
Human Behaviors ◽  
Indirect Encoding

The current surveillance systems must identify the continuous human behaviors to detect various events from video streams. To enhance the performance of event recognition, in this chapter, we propose a distributed low-cost smart cameras system, together with a machine learning technique to detect abnormal events through analyzing the sequential behaviors of a group of people. Our system mainly includes a simple but efficient strategy to organize the behavior sequence, a new indirect encoding scheme to represent a group of people with relatively few features, and a multi-camera collaboration strategy to perform collective decision making for event recognition. Experiments have been conducted and the results confirm the reliability and stability of the proposed system in event recognition.

ANALYSIS OF HUMAN WALKING TRAJECTORIES FOR SURVEILLANCE

2004 ◽  
Vol 01 (02) ◽  
pp. 169-189
Author(s):  
KA KEUNG LEE ◽  
YANGSHENG XU
Keyword(s):  
Real Time ◽  
Similarity Measure ◽  
Real Life ◽  
Visual Surveillance ◽  
Longest Common Subsequence ◽  
Human Walking ◽  
Support Vector ◽  
Surveillance Systems ◽  
Boundary Values ◽  
Variable Boundary

Surveillance of public places has become a worldwide concern in recent years. The ability to identify abnormal human behaviors in real-time is fundamental to the success of intelligent surveillance systems. The recognition of abnormal and suspicious human walking patterns is an important step towards the achievement of this goal. In this research, we develop an intelligent visual surveillance system that can classify normal and abnormal human walking trajectories in outdoor environments by learning from demonstration. The system takes into account both the local and global characteristics of the observed trajectories and is able to identify their normality in real-time. By utilizing support vector learning and a similarity measure based on hidden Markov models, the developed system has produced satisfactory results on real-life data during testing. Moreover, we utilize the approach of longest common subsequence (LCSS) in determining the similarity between different types of walking trajectories. In order to establish the position and speed boundaries required for the similarity measure, we compare the performance of a number of approaches, including fixed boundary values, variable boundary values, learning boundary by support vector regression, and learning boundary by cascade neural networks.

scholarly journals Improving TDWZ Correlation Noise Estimation: A Deep Learning based Approach

2020 ◽  
Author(s):  
Vũ Hữu Tiến ◽  
Thao Nguyen Thi Huong ◽  
San Vu Van ◽  
Xiem HoangVan
Keyword(s):  
Deep Learning ◽  
Activation Function ◽  
Vital Role ◽  
Visual Surveillance ◽  
Noise Model ◽  
Perceptual Quality ◽  
Surveillance Systems ◽  
Compression Performance ◽  
Laplacian Distribution ◽  
Correlation Noise

Transform domain Wyner-Ziv video coding (TDWZ) has shown its benefits in compressing video applications with limited resources such as visual surveillance systems, remote sensing and wireless sensor networks. In TDWZ, the correlation noise model (CNM) plays a vital role since it directly affects to the number of bits needed to send from the encoder and thus the overall TDWZ compression performance. To achieve CNM with high accurate for TDWZ, we propose in this paper a novel CNM estimation approach in which the CNM with Laplacian distribution is adaptively estimated based on a deep learning (DL) mechanism. The proposed DL based CNM includes two hidden layers and a linear activation function to adaptively update the Laplacian parameter. Experimental results showed that the proposed TDWZ codec significantly outperforms the relevant benchmarks, notably by around 35% bitrate saving when compared to the DISCOVER codec and around 22% bitrate saving when compared to the HEVC Intra benchmark while providing a similar perceptual quality.

scholarly journals SECURITY EVENT RECOGNITION FOR VISUAL SURVEILLANCE

2017 ◽  
Vol IV-1/W1 ◽  
pp. 19-26 ◽  
Author(s):  
W. Liao ◽  
C. Yang ◽  
M. Ying Yang ◽  
B. Rosenhahn
Keyword(s):  
State Of The Art ◽  
Visual Surveillance ◽  
Event Recognition ◽  
Event Analysis ◽  
Surveillance Video ◽  
Practical Applications ◽  
Special Events ◽  
Security Event ◽  
The Given ◽  
Reliable Methods

With rapidly increasing deployment of surveillance cameras, the reliable methods for automatically analyzing the surveillance video and recognizing special events are demanded by different practical applications. This paper proposes a novel effective framework for security event analysis in surveillance videos. First, convolutional neural network (CNN) framework is used to detect objects of interest in the given videos. Second, the owners of the objects are recognized and monitored in real-time as well. If anyone moves any object, this person will be verified whether he/she is its owner. If not, this event will be further analyzed and distinguished between two different scenes: moving the object away or stealing it. To validate the proposed approach, a new video dataset consisting of various scenarios is constructed for more complex tasks. For comparison purpose, the experiments are also carried out on the benchmark databases related to the task on abandoned luggage detection. The experimental results show that the proposed approach outperforms the state-of-the-art methods and effective in recognizing complex security events.

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