Abnormal Crowd Behavior Detection Using Motion Information Images and Convolutional Neural Networks

Download

11.pdf

Date

2020

Author

Direkoğlu, Cem

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

79
views

165
downloads

We introduce a novel method for abnormal crowd event detection in surveillance videos. Particularly, our work focuses on panic and escape behavior detection that may appear because of violent events and natural disasters. First, optical flow vectors are computed to generate a motion information image (MII) for each frame, and then MIIs are used to train a convolutional neural network (CNN) for abnormal crowd event detection. The proposed MII is a new formulation that provides a visual appearance of crowd motion. The proposed MIIs make the discrimination between normal and abnormal behaviors easier. The MII is mainly based on the optical flow magnitude, and angle difference computed between the optical flow vectors in consecutive frames. A CNN is employed to learn normal and abnormal crowd behaviors using MIIs. The MII generation, and the combination with a CNN is a new approach in the context of abnormal crowd behavior detection. Experiments are performed on commonly used datasets such as UMN and PETS2009. Evaluation indicates that our method achieves the best results.

Subject Keywords

General Engineering, General Materials Science, General Computer Science, Crowd behavior analysis, Anomaly detection, Motion information image, Convolutional neural network

URI

https://hdl.handle.net/11511/52303

Journal

IEEE Access

DOI

https://doi.org/10.1109/access.2020.2990355

Collections

Department of Electrical and Electronics Engineering, Article

Suggestions

OpenMETU
Core

Abnormal Crowd Behavior Detection Using Novel Optical Flow-Based Features Direkoglu, Cem; Sah, Melike; O'Connor, Noel E. (2017-09-01) In this paper, we propose a novel optical flow based features for abnormal crowd behaviour detection. The proposed feature is mainly based on the angle difference computed between the optical flow vectors in the current frame and in the previous frame at each pixel location. The angle difference information is also combined with the optical flow magnitude to produce new, effective and direction invariant event features. A one-class SVM is utilized to learn normal crowd behavior. If a test sample deviates si...
Automated crowd behavior analysis for video surveillance applications Güler, Püren; Temizel, Alptekin; Taşkaya Temizel, Tuğba; Department of Information Systems (2012) Automated analysis of a crowd behavior using surveillance videos is an important issue for public security, as it allows detection of dangerous crowds and where they are headed. Computer vision based crowd analysis algorithms can be divided into three groups; people counting, people tracking and crowd behavior analysis. In this thesis, the behavior understanding will be used for crowd behavior analysis. In the literature, there are two types of approaches for behavior understanding problem: analyzing behavi...
Analysis of pulse diversity in radar systems Keçelioğlu, Umut; Koç, Seyit Sencer; Department of Electrical and Electronics Engineering (2006) In this thesis, the pulse diversity technique in radar systems in high clutter environments is investigated. In this technique, different pulse compression methods are used in each pulse in the transmitted burst to increase the unambiguous range. In pulse diversity, the design of filters used in the receiver is as important as designing the transmitted waveform. At the output of pulse-burst filter that processes pulse-by-pulse, as many channels as the pulses in the burst occur. Each of these channels is mat...
A FRAMEWORK FOR DETECTING COMPLEX EVENTS IN SURVEILLANCE VIDEOS Onal, Itir; Kardas, Karani; Rezaeitabar, Yousef; Bayram, Ulya; Bal, Murat; Ulusoy, İlkay; Cicekli, Nihan Kesim (2013-07-19) This paper presents a framework for detecting complex events in surveillance videos. Moving objects in the foreground are detected in the object detection component of the system. Whether these foregrounds are human or not is decided in the object recognition component. Then each detected object is tracked and labeled in the object tracking component, in which true labeling of objects in the occlusion situation is also provided. The extracted information is fed to the event detection component. Rule based e...
Deep convolutional neural networks for airport detection in remote sensing images Budak, Umit; Sengur, Abdulkadir; Halıcı, Uğur (2018-05-05) This study investigated the use of deep convolutional neural networks (CNNs) in providing a solution for the problem of airport detection in remote sensing images (RSIs). In recent years, Deep CNNs have gained much attention with numerous applications having been undertaken in the area of computer vision. Researchers generally approach airport detection as a pattern recognition problem, in which first various distinctive features are extracted, and then a classifier is adopted to detect airports. CNNs not o...

Citation Formats

C. Direkoğlu, “Abnormal Crowd Behavior Detection Using Motion Information Images and Convolutional Neural Networks,” IEEE Access, pp. 80408–80416, 2020, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/52303.