Detection of suspicious activity and estimate of risk from human behavior shot by surveillance camera

Takai, Miwa

doi:10.1109/nabic.2010.5716350

Cited by 15 publications

(10 citation statements)

References 9 publications

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“…A very little work has been done on detecting suspicious activity and questionable observer [1,13,17] from surveillance camera. Some work has been done to detect violent crowd behavior [7,9].…”

Section: Related Workmentioning

confidence: 99%

“…Facial expression and human behavior detection is done in [6,19,21], which are also vision based approaches. These works also contains some challenges and limitations: some works needed fixed camera shoots from same angle and distance [1]; in some cases with the increasing training dataset, the time required also increased by some margin [13]; some detection is harmed by variations in pose, illumination, and facial expression throughout a single video and between different videos, that can affect face appearance and, hence, complicate questionable observer detection as well and also the video evidence may be recorded by camera phones or surveillance cameras and so the quality of the face image sequences can be very low [17] and the "object-based methods" for understanding crowd behaviors faces considerable complexity in detecting objects, tracking trajectories and recognizing activities in dense crowds where the whole process is affected by occlusions [9].…”

Section: Related Workmentioning

confidence: 99%

“…Then we load our input video (or frame) in gray scale mode. After that we find the midpoint in each frame by using equations (1) and (2). Fig.…”

Section: A Experimental Set Upmentioning

confidence: 99%

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Efficient Framework Using Morphological Modeling for Frequent Iris Movement Investigation towards Questionable Observer Detection

Anisuzzaman¹,

Saif²

2018

IJIGSP

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This research presents a framework to detect a questionable observer depending on a specific activity named "frequent iris movement". We have focused on some activities and behaviors upon which we can classify one as questionable. So this research area is not only an important part of computer vision and artificial intelligence, but also a major part of human activity recognition (HAR). We have used Haar Cascade Classifier to detect irises of both left and right eyes. Then running some morphological operation we have detected the midpoint between left and right irises; and based on some characteristics of midpoint movement we have detected a specific activityfrequent iris movement. Depending on this activity we are declaring someone as questionable observer. To validate this research we have created our own dataset with 86 videos, where 15 individuals have volunteered. We have achieved an accuracy of 90% for the first 100 frames or 3.33 seconds of each of our videos and an accuracy of 93% for the first 150 frames or 5.00 seconds of each of our videos. No work has been done yet on basis of this specific activity to detect someone as questionable and furthermore our work outperforms most of the existing work on questionable observe detection and suspicious activity recognition.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Efficient Framework Using Morphological Modeling for Frequent Iris Movement Investigation towards Questionable Observer Detection

Anisuzzaman¹,

Saif²

2018

IJIGSP

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“…Anomalous activity detection in surveillance videos can present diverse opportunities for both enhancing situation awareness (Takai, 2010) and mining critical forensic evidence (Ouivirach et al, 2013). In the last few years, the number of security cameras installed at various surveillance sites across the world has increased in leaps and bounds.…”

Section: Introductionmentioning

confidence: 99%

Scene Representation and Anomalous Activity Detection using Weighted Region Association Graph

Dogra

Reddy

Subramanyam³

et al. 2015

Proceedings of the 10th International Conference on Computer Vision Theory and Applications

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In this paper we present a novel method for anomalous activity detection using systematic trajectory analysis. First, the visual scene is segmented into constituent regions by attaching importances based on motion dynamics of targets in that scene. Further, a structured representation of these segmented regions in the form of a region association graph (RAG) is constructed. Finally, anomalous activity is detected by benchmarking the target's trajectory against the RAG. We have evaluated our proposed algorithm and compared it against competent baselines using videos from publicly available as well as in-house datasets. Our results indicate high accuracy in localizing anomalous segments and demonstrate that the proposed algorithm has several compelling advantages when applied to scene analysis in autonomous visual surveillance.

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“…Especially, explosive and chemical attacks using unattended objects are occurring repeatedly in public areas, such as airports, train or subway stations, bus terminals, etc. Many studies about prevention or prediction of threats have been in progress for people safety [3,14,17]. The importance of video-based surveillance systems is increasing everyday.…”

Section: Introductionmentioning

confidence: 99%

Unattended object detection based on edge-segment distributions

Kim

Rivera

Ryu

et al. 2014

2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)

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Unattended object detection is an important task in surveillance. Thus, we propose a new method to detect unattended object by modeling the objects as newly learned temporal background. We use edge-segments to model the structural changes in the scene. Specifically, we construct distributions of these edge-segments to analyze the scene, and to segment its different components: background, foreground, and the interesting new objects. Additionally, we propose a clustering algorithm to recover the unattended objects from a set of edges based on the assumption that spatially close edges come from the same object. Our experiments on several datasets validate our proposed method.

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Detection of suspicious activity and estimate of risk from human behavior shot by surveillance camera

Cited by 15 publications

References 9 publications

Efficient Framework Using Morphological Modeling for Frequent Iris Movement Investigation towards Questionable Observer Detection

Efficient Framework Using Morphological Modeling for Frequent Iris Movement Investigation towards Questionable Observer Detection

Scene Representation and Anomalous Activity Detection using Weighted Region Association Graph

Unattended object detection based on edge-segment distributions

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