Editorial Notes
NOTICE OF CONCERN: ACM has received evidence that casts doubt on the integrity of the peer review process for the ICIA 2016 Conference. As a result, ACM is issuing a Notice of Concern for all papers published and strongly suggests that the papers from this Conference not be cited in the literature until ACM's investigation has concluded and final decisions have been made regarding the integrity of the peer review process for this Conference.
ABSTRACT
In the field of image processing, it is more complex and challenging task to detect the Human motion in the video and recognize their actions from the video sequences. A novel approach is presented in this paper to detect the human motion and recognize their actions. By tracking the selected object over consecutive frames of a video or image sequences, the different Human actions are recognized. Initially, the background motion is subtracted from the input video stream and its binary images are constructed. Using spatiotemporal interest points, the object which needs to be monitored is selected by enclosing the required pixels within the bounding rectangle. The selected foreground pixels within the bounding rectangle are then tracked using edge tracking algorithm. The features are extracted and using these features human motion are detected. Finally, the different human actions are recognized using K-Nearest Neighbor classifier. The applications which uses this methodology where monitoring the human actions is required such as shop surveillance, city surveillance, airports surveillance and other important places where security is the prime factor. The results obtained are quite significant and are analyzed on the datasets like KTH and Weizmann dataset, which contains actions like bending, running, walking, skipping, and hand-waving.
- Murat EKINCI, Eyup GEDIKLI, 2005. Silhouette Based Human Motion and Action Detection and Analysis for Real-Time Automated Video Surveillance. Turk J Elec Engin. Volume 13, No.2.Google Scholar
- Nazh Ikizler and Põnar Duygulu, 1999. Human Action Recognition Using Distribution of Oriented Rectangular Patches. Computer vision and pattern recognition (CVPR.05). Volume1, pp 886--893.Google Scholar
- Nazh Ikizler and Põnar Duygulu, 2009. Histograms of oriented rectangles: A new pose descriptor for human action recognition. Image and vision computing. Volume 27, Issue 10, pp 1515--1526. Google ScholarDigital Library
- Chunfeng Yuan, Weiming Hu, Xi Li, Stephen Maybank, Guan Luo, 2004. Human Action Recognition under Log-Euclidean Riemannian Metric. Computer Vision ACCV-2009, 9th Asian Conference on Computer Vision. Google ScholarDigital Library
- Luciano Spinello Rudolph Triebel Roland Siegwart, 2008, Multimodal People Detection and Tracking in Crowded Scenes, Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence, pp-1409--1414 Google ScholarDigital Library
- Link to Weizmann Dataset: http://www.wisdom.weizmann.ac.il/~vision/SpaceTime Actions.html.Google Scholar
- R.Venkatesh Babu and R.Hariharan, 2009. Image processing, video surveillance, and security related applications using parallel machines. NAL-PD-FS-0916 National Aerospace LaboratoriesGoogle Scholar
- Link to KTH Dataset http://www.nada.kth.se/cvap/actions/Google Scholar
Recommendations
Real-time video surveillance based on combining foreground extraction and human detection
MMM'08: Proceedings of the 14th international conference on Advances in multimedia modelingIn this paper, we present an adaptive foreground object extraction algorithm for real-time video surveillance, in conjunction with a human detection technique applied in the extracted foreground regions by using AdaBoost learning algorithm and ...
A consensus-based method for tracking: Modelling background scenario and foreground appearance
Modelling of the background (''uninteresting parts of the scene''), and of the foreground, play important roles in the tasks of visual detection and tracking of objects. This paper presents an effective and adaptive background modelling method for ...
Event detection in video using motion analysis
ARTEMIS '10: Proceedings of the first ACM international workshop on Analysis and retrieval of tracked events and motion in imagery streamsDigital video is being used widely in a variety of applications such as entertainment, surveillance and security. Large amount of video in surveillance and security requires systems capable of processing video to automatically detect and recognize ...
Comments