Proceedings ArticleDOI
Suspicious object detection in surveillance videos for security applications
Trupti M. Pandit,P. M. Jadhav,Anuradha C. Phadke +2 more
- Vol. 2016, pp 1-5
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TLDR
An algorithm for detecting unattended and unknown object using background subtraction and morphological filtering to improve the safety and security of the public areas and to automatically detect the suspicious object.Abstract:
Now a day security is a major issue in public places such as railway stations, airports, shopping malls and in crowded areas. Unattended object detection is one of essential tasks in video surveillance system. This paper proposes an algorithm for detecting unattended and unknown object using background subtraction and morphological filtering. The main goal of the algorithm is to automatically detect the suspicious object and to improve the safety and security of the public areas. The system takes frames from video which is captured by a static camera as input and subtraction of foreground and background is done using thresholding techniques. To improve the detected region, morphological operation is used in the process. The system recognizes the reasons of interest, creates blobs of objects, labels the blobs and finally gives a warning when suspicious objects are detected.read more
Citations
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Proceedings ArticleDOI
BayesOD: A Bayesian Approach for Uncertainty Estimation in Deep Object Detectors
TL;DR: Experiments performed on four common object detection datasets show that BayesOD provides uncertainty estimates that are better correlated with the accuracy of detections, manifesting as a significant reduction of 9.77%-13.13% on the minimum Gaussian uncertainty error metric.
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BayesOD: A Bayesian Approach for Uncertainty Estimation in Deep Object Detectors.
TL;DR: BayesOD as discussed by the authors reformulates the standard object detector inference and non-maximum suppression components from a Bayesian perspective, and provides uncertainty estimates that are better correlated with the accuracy of detections.
Proceedings ArticleDOI
Intelligent Video Analytic for Suspicious Object Detection : A Systematic Review
Hanavi,Fadhil Hidayat +1 more
TL;DR: In this paper, the authors comprehensively and systematically review the literature on applying machine learning for object detection and video surveillance systems published between 2010 and 2020 and highlight the challenges and opportunities for suspicious object detection research using video analytics in the future.
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Object Counting using KAZE Features Under Different Lighting Conditions for Inventory Management
TL;DR: The experimental results state that the proposed approach outperforms the existing approaches of object counting in automated inventory management in the presence of different lighting conditions such as low-light or dim-light and bright light.
Proceedings ArticleDOI
Bird Eyes: A Cloud-Based Object Detection System for Customisable Surveillance
TL;DR: Bird Eyes defers the heavy processing requirements of an object detection system away from the user and into the cloud, allowing for access to customisable surveillance with off-the-shelf hardware.
References
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Book ChapterDOI
Non-parametric Model for Background Subtraction
TL;DR: A novel non-parametric background model that can handle situations where the background of the scene is cluttered and not completely static but contains small motions such as tree branches and bushes is presented.
Detecting Abandoned Luggage Items in a Public Space
TL;DR: This paper presents a solution to the problem of determining if a luggage item is left unattended by its owners using a trans-dimensional Markov Chain Monte Carlo tracking model suited for use in generic blob tracking tasks.
Proceedings ArticleDOI
Robust Unattended and Stolen Object Detection by Fusing Simple Algorithms
J.C. San Miguel,José M. Martínez +1 more
TL;DR: Experimental results show that the fusion-based approach increases the detection reliability as compared to the detectors and performs considerably well across a variety of multiple scenarios operating at realtime.
Proceedings ArticleDOI
Action detection using multiple spatial-temporal interest point features
TL;DR: A new approach which combines Gaussian Mixture Model with Branch-and-Bound search to efficiently locate the action of interest is proposed, which outperforms the state-of-the-art methods.
Proceedings ArticleDOI
Generative model for abandoned object detection
TL;DR: An algorithm for abandoned object detection based on generative model of low level features that has been verified in 29 challenging scenes and produces very low false alarms and missing detection.
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