Date of Publication :7th May 2016
Abstract: In real world applications, securing video is more important due to the happening of unusual events. Moving object detection and tracking is difficult in low resolution video and it became a challenging task due to loss of discriminative detail in visual appearance. The existing methods use super resolution techniques to enhance the low resolution video. But these methods are not economical. The cost further increases for event detection. This paper presents an algorithm to detect unusual events without using any super resolution techniques and it is useful for security purpose where low resolution cameras are used due to low cost. This algorithm uses background subtraction technique to detect object of interest from the background. This approach uses close morphological operation with structuring element in pre-processing step. Proposed algorithm is able to detect unusual events such as overcrowding or fight in low resolution video by using standard deviation. It process low resolution frames, so this is fast and helpful in video surveillance system where low resolution cameras are used. Since the use of classifiers is avoided in our algorithm, there is no need of training requirement.
Reference :
-
1) Sudhir Goswami, Jyoti Goswami, Nagresh Kumar, “ Unusual Event Detection in Low Resolution Video for enhancing ATM security,” in Signal Processing and Integrated Networks (SPIN), 2015 IEEE 2nd International Conference on. IEEE, 2015, pp. 848 - 853.
2) Burkey Birant Orten, “Moving Object Identification and Event Recognition in Video Surveillance Systems”, MS Thesis in Electrical and Electronics department in METU, 2005.
3) Kamal Kant Verma, Pradeep Kumar, AnkitTomar, “Analysis of Moving Object Detection and Tracking in Video Surveillance System,” in Computing for Sustainable Global Development (INDIACom), 2015 IEEE International Conference on. IEEE, 2015, pp. 1758 - 1762
4) [Online].Availabe:https://en.wikipedia.org/wiki/Backg round_subtraction
5) Fujiyoshi, H., Lipton, A.J., “Real-time human motion analysis by Image skeletonization.”, in Applications of Computer Vision, 1998, pp.15- 21
6) [Online]. Available: http://wiki.eigenvector.com / index.php?title= Image_Pre-Processing_Methods
7) Donovan H. Parks and Sidney S. Fels, “Evaluation of background subtraction Algorithm with Postprocessing”, in IEEE Fifth International Conference on Advanced Video and Signal Based Surveillance, 2008, pp. 192 – 199.
8) Sugandi, B. ,“A Block Matching Technique for Object Tracking
9) Based on Peripheral Increment Sign Correlation Image”,
10) International Conference on Computer and Communication
11) Engineering, 2008, pp. 113-117.
12) [Online]Available:http://en.wikipedia.org/wiki/Structu ring_element
13) [Online].Available:http://en.wikipedia.org/wiki/standa rd_deviation.