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Nagmode, Manoj S.
- Drowsiness Fatigue Detection of Driver in Real Time
Authors
1 MIT College of Engineering, Pune, IN
Source
Digital Image Processing, Vol 6, No 7 (2014), Pagination: 306-312Abstract
Nowadays, driver drowsiness is very big issue for the road accident and which causes serious deaths and severe injuries. There are plenty of methods to detect driver drowsiness or drowsiness fatigue. Our focus point for this paper is to present a fatigue of drowsiness detection. Here we are detecting the eye location of the driver. When driver’s eye closed for more than predefined time then the driver is supposed to be said feeling drowsy and then an alarming condition is activated.
Keywords
Success Rate, Blink Detection, Open CV, Connected Component, DR, Eye Tracking, FAR.
- Moving Object Detection and Tracking based on Correlation and Wavelet Transform Techniques to Optimize Processing Time
Authors
1 MIT College of Engineering, Pune, IN
Source
Digital Image Processing, Vol 5, No 1 (2013), Pagination: 36-43Abstract
Moving object detection and tracking has attracted significant research interest in recent years. It has many application such as traffic monitoring, military, medicine and biological sciences etc. detection and tracking of moving object in video sequences can offer significant benefit to motion analysis.
In this paper, two algorithms for moving object detection and tracking are proposed. In the first algorithm cross correlation is used and in second algorithm, Wavelet transform based technique is used for detecting and tracking of the moving object. Cross Correlation is applied to each sub frame after taking the difference between the two frames. The minimum value of Cross Correlation indicates the presence of moving object. Location of the moving object is obtained by performing component connected analysis and morphological processing. After that the centroid calculation is used to track the moving object. The second algorithm is based on wavelet decomposition (i.e. multi resolution analysis) for the detection of moving object and then centroid calculation is used to track that object.
Qualitative and quantitative results in terms of Detection Rate (DR), False Alarm Rate (FAR) and average processing time per frame are given. The proposed algorithms are compared with the established methods based on simple difference and background subtraction. Comparison shows that methods based on cross correlation and wavelet decomposition outperform the previous methods in Success Rate. Also, it is observed that processing time of cross correlation based method is better than wavelet decomposition based method.