Tracking and Abnormal Behavior Detection in Video Surveillance Using Optical Flow and Neural Networks
| Title | Tracking and Abnormal Behavior Detection in Video Surveillance Using Optical Flow and Neural Networks |
| Publication Type | Conference Paper |
| Year of Publication | 2014 |
| Authors | Rasheed, N., Khan, S.A., Khalid, A. |
| Conference Name | Advanced Information Networking and Applications Workshops (WAINA), 2014 28th International Conference on |
| Date Published | May |
| Keywords | abnormal behavior detection, Adaptive optics, chaotic movement, Computer vision, feature selection, feed forward neural network, feedforward neural nets, FGMM model, Foreground Detection, foreground detection with Gaussian mixture model, Gaussian Mixture Models, Gaussian processes, image motion analysis, image sequences, Lucas-Kanade approach, mixture models, Neural Network, Neural networks, normal movement, object detection, Optical computing, optical flow, Optical imaging, real time videos, Streaming media, synthesized videos, targets identification, video frames, video surveillance |
| Abstract | An abnormal behavior detection algorithm for surveillance is required to correctly identify the targets as being in a normal or chaotic movement. A model is developed here for this purpose. The uniqueness of this algorithm is the use of foreground detection with Gaussian mixture (FGMM) model before passing the video frames to optical flow model using Lucas-Kanade approach. Information of horizontal and vertical displacements and directions associated with each pixel for object of interest is extracted. These features are then fed to feed forward neural network for classification and simulation. The study is being conducted on the real time videos and some synthesized videos. Accuracy of method has been calculated by using the performance parameters for Neural Networks. In comparison of plain optical flow with this model, improved results have been obtained without noise. Classes are correctly identified with an overall performance equal to 3.4e-02 with & error percentage of 2.5. |
| URL | https://ieeexplore.ieee.org/document/6844614/ |
| DOI | 10.1109/WAINA.2014.18 |
| Citation Key | 6844614 |
- Lucas-Kanade approach
- video surveillance
- video frames
- targets identification
- synthesized videos
- Streaming media
- real time videos
- Optical imaging
- optical flow
- Optical computing
- object detection
- normal movement
- Neural networks
- neural network
- mixture models
- abnormal behavior detection
- image sequences
- image motion analysis
- Gaussian processes
- Gaussian Mixture Models
- foreground detection with Gaussian mixture model
- Foreground Detection
- FGMM model
- feedforward neural nets
- feed forward neural network
- Feature Selection
- computer vision
- chaotic movement
- Adaptive optics