Improved background subtraction techniques for security in video applications

Srinivasan, K.; Porkumaran, K.; Sainarayanan, G.

doi:10.1109/icasid.2009.5276945

Cited by 81 publications

(46 citation statements)

References 2 publications

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“…While the second approach is proven to be successful in several surveillance algorithms, it uses the dynamic or static background for effective detection of the foreground object. Figure 1, shows an overview of the background modeling and subtraction system as in [1]. 2) Frame conversion: After capturing the video, it is converted into frames of suitable type so that further processing could be done conveniently.…”

Section: • Background Modeling and Subtractionmentioning

confidence: 99%

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Unusual event detection in low resolution video for enhancing ATM security

Goswami¹,

Goswami

kumar³

2015

2015 2nd International Conference on Signal Processing and Integrated Networks (SPIN)

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In real world applications, tracking target in low resolution video is a challenging task because there is loss of discriminative detail in the visual appearance of moving object. The existing methods are mostly based on the enhancement of LR (low resolution) video by super resolution techniques. But these methods require high computational cost. This cost further increases if we are dealing with events detection. In this paper we present an algorithm which is able to detect unusual events without such type of conversion and well suited for enhancement of security of ATMs where conventional low resolution cameras are generally used due to their low cost. Proposed algorithm only uses close morphological operation with disk like structuring element in the preprocessing steps to cope up with low resolution video. It further uses rolling average background subtraction technique to detect foreground object from dynamic background in a scene. Our proposed algorithm is able to recognize the occurrence of uncommon events such as overcrowding or fight in the low resolution video simply by using statistical property, standard deviation of moving objects. It is fast enough because it process low resolution frames and could be helpful in surveillance system for enhancing the security of ATMs where conventional camera of low resolution are still used. It does not use any classifier and avoids the requirement of training the system initially.

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Section: • Background Modeling and Subtractionmentioning

confidence: 99%

“…as in [1]. But there are many challenges which produce hurdles in the improvement of these applications.…”

Section: Introductionmentioning

confidence: 99%

Unusual event detection in low resolution video for enhancing ATM security

Goswami¹,

Goswami

kumar³

2015

2015 2nd International Conference on Signal Processing and Integrated Networks (SPIN)

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“…After selecting the input image we have to focus on the target image . [7] After getting both input image and target image we compare the size of both the image. If the size of input image is greater than target image then we proceed with our system otherwise we are going to focus on the input image.…”

Section: Comparison Between Various Methodsmentioning

confidence: 99%

Object Detection and Tracking: A Review

Joshi¹

2016

ijetst

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“…Recursive techniques [3,8] does not keep up a buffer in estimating a background, Instead it recursively keeps updating a single model for each of the input frame. This results in input frames having an effect towards the present background model.…”

Section: Recursive Algorithmmentioning

confidence: 99%

“…Non-recursive-techniques in [8,3] makes use of slidingwindow method to estimate the background. It also stores the buffer of a preceding video frames which in turn evaluates the so called background image which is based on temporal variations in each of the pixel that are buffered.…”

Section: Non-recursive Algorithmmentioning

confidence: 99%

Statistical Review of Various Object Detection Techniques

N.S¹,

Sheshadri²

2016

IJSEA

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Abstract:The main and important objective of tracking an object is to segment a ROI from a given video scene. Once the object is tracked it is continued to track its motion occlusion and most importantly its position. Detection and classifications of objects are the previous steps to track an object in a given video. Object detection process is done to check the existence or presence of objects in a given video and also to exactly locate the existed object. After detection, the objects that are detected are classified into varieties of categories namely humans, pedestrian, vehicles and also other objects which are under motion. Object tracking process is done by monitoring the objects temporal as well as spatial changes throughout a given video sequence which includes the presence, size and position etc. Object tracking has been used in numerous applications like video surveillance, artificial intelligence, traffic monitoring and also in video animations. This work presents a short-term survey of various object detection algorithms which is available in literature survey together with analysis and also a comparative learning of diverse detection techniques used.

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Improved background subtraction techniques for security in video applications

Cited by 81 publications

References 2 publications

Unusual event detection in low resolution video for enhancing ATM security

Unusual event detection in low resolution video for enhancing ATM security

Object Detection and Tracking: A Review

Statistical Review of Various Object Detection Techniques

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