Feature Detection and Tracking for Extraction of Crowd Dynamics

Date

2013-01-01

Author

Gunduz, Ayse Elvan
Temizel, Alptekin
Temizel, Tugba Taskaya

Metadata

Show full item record

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

Item Usage Stats

29
views

0
downloads

Extraction of crowd dynamics from video is the fundamental step for automatic detection of abnormal events. However, it is difficult to obtain sufficient performance with object tracking due to occlusions and insufficient resolution of the objects in the scene. As a result, optical flow or feature tracking methods are preferred in crowd videos. These applications also require algorithms to work in real-time. In this work, we investigated the applicability and performance of feature detection and tracking algorithms in crowd videos. The algorithms that were tested in this paper include Scale Invariant Feature Transform (SIFT) and Speeded-Up Robust Features (SURF) as well as relatively newer approaches Binary Robust Independent Elementary Features (BRIEF) and Oriented Fast and Rotated Brief (ORB). These algorithms have been tested with videos having different crowd densities and comparative results of their accuracy and computational performance have been reported. The results show that BRIEF is computationally faster than the others, allowing real-time operation, and comparable with other algorithms regarding matching accuracy.

Subject Keywords

Video surveillance, Feature extraction, Feature matching, Computer vision

URI

https://hdl.handle.net/11511/31406

DOI

https://doi.org/10.1109/siu.2013.6531572

Conference Name

21st Signal Processing and Communications Applications Conference (SIU)

Collections

Graduate School of Informatics, Conference / Seminar

Suggestions

OpenMETU
Core

Feature Extraction and Object Classification for Target Identification at Wireless Multimedia Sensor Networks Civelek, Muhsin; Yilmazer, Cengiz; Yazıcı, Adnan; Korkut, Fazli Oncul (2014-04-25) In this paper, it is investigated the processes for automatic identification of the targets without personnel intervention in wireless multimedia sensor networks. Methods to extract the features of the object from the multimedia data and to classify the target type based on the extracted features are proposed within the scope of this study. The success of the proposed methods are tested by implementing a Matlab application and the results are presented in this paper
INTEGRATED INSTANCE-BASED AND KERNEL METHODS FOR POWER QUALITY KNOWLEDGE MODELING Guder, Mennan; Salor, Ozgul; Cadirci, Isik (2010-10-28) In this paper, an integrated knowledge discovery strategy for high dimensional spatial power quality event data is proposed. Real time, distributed measuring of the electricity transmission system parameters provides huge number of time series power quality events. The proposed method aims to construct characteristic event distribution and interaction models for individual power quality sensors and the whole electricity transmission system by considering feasibility, time and accuracy concerns. In order to ...
Robust Automatic Target Recognition in FLIR imagery Soyman, Yusuf (2012-04-24) In this paper, a robust automatic target recognition algorithm in FLIR imagery is proposed. Target is first segmented out from the background using wavelet transform. Segmentation process is accomplished by parametric Gabor wavelet transformation. Invariant features that belong to the target, which is segmented out from the background, are then extracted via moments. Higher-order moments, while providing better quality for identifying the image, are more sensitive to noise. A trade-off study is then perform...
Comparison of Cuboid and Tracklet Features for Action Recognition on Surveillance Videos Bayram, Ulya; Ulusoy, İlkay; Cicekli, Nihan Kesim (2013-01-01) For recognition of human actions in surveillance videos, action recognition methods in literature are analyzed and coherent feature extraction methods that are promising for success in such videos are identified. Based on local methods, most popular two feature extraction methods (Dollar's "cuboid" feature definition and Raptis and Soatto's "tracklet" feature definition) are tested and compared. Both methods were classified by different methods in their original applications. In order to obtain a more fair ...
An unsupervised method for anomaly detection from crowd videos Guler, Puren; Temizel, Alptekin; Temizel, Tugba Taskaya (2013-01-01) Anomaly detection from crowd videos is an issue that is becoming more important due to the difficulties in maintaining the public security in crowded places. Surveillance videos has a significant role for enabling the real time analysis of the captured events occurring in crowded places. This paper presents a method that detects anomalies in crowd in real-time using computer vision and machine learning techniques. The proposed method consists of extracting the crowd behavior properties (velocity, direction)...

Citation Formats

A. E. Gunduz, A. Temizel, and T. T. Temizel, “Feature Detection and Tracking for Extraction of Crowd Dynamics,” presented at the 21st Signal Processing and Communications Applications Conference (SIU), CYPRUS, 2013, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/31406.