Real-Time Deep Learning Method for Abandoned Luggage Detection in Video

Smeureanu, Sorina; Ionescu, Radu Tudor

doi:10.23919/eusipco.2018.8553156

Cited by 28 publications

(11 citation statements)

References 24 publications

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“…Smeureanu and Ionescu [16] perform stationary object detection based on background subtraction and motion estimation. And they tried to improve the detection accuracy of abandoned objects by applying a cascade of convolutional neural networks (CNN).…”

Section: Related Workmentioning

confidence: 99%

Detection of Abandoned and Stolen Objects Based on Dual Background Model and Mask R-CNN

Park

Joo

2020

IEEE Access

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Dual background models have been widely used for detecting stationary objects in video surveillance systems. However, there is a problem that both abandoned and stolen objects are equally detected as stationary objects, making it difficult to distinguish them. Another problem is the ghost region created by shadow shift or light changes, which makes the discrimination issue more complicated. In this paper, we present an efficient method to distinguish abandoned objects, stolen objects, and ghost regions in the surveillance video. This method contains two main strategies: the first one is the dual background model for extracting candidate stationary objects, the second one is object segmentation based on mask regions with CNN features (Mask R-CNN) for providing the object mask information. The basic idea is: given a candidate stationary object from the background model, it is checked whether a corresponding segmented object exists in the current video frame or the previous background frame to take into account the current and past situations. And the final state of the candidate stationary object is determined by considering various situations through the comparative analysis technique presented in this paper. The proposed algorithm has qualitatively experimented with our own dataset focusing on the discrimination issue, which generated satisfactory results. Therefore, it is expected to be widely applied to automatic detection of stolen objects as well as abandoned objects in open environments such as exhibition halls and public parks where existing intrusion detection-based security services are difficult to be deployed. INDEX TERMS Abandoned object detection, stolen object detection, ghost region, dual background model, mask R-CNN.

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Section: Related Workmentioning

confidence: 99%

Detection of Abandoned and Stolen Objects Based on Dual Background Model and Mask R-CNN

Park

Joo

2020

IEEE Access

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“…If illumination changes happen, foreground objects remain un-absorbed in the long-term foreground and a large number of stationary objects are wrongly generated. Recently, deep learning techniques have been attempted to some researches [10]. However, these techniques have problems that the range of objects that can be identified by the deep learning model is limited and the computational costs are high.…”

Section: Related Workmentioning

confidence: 99%

Robust Real-time Detection of Abandoned Objects using a Dual Background Model

2020

KSII TIIS

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Detection of abandoned objects for smart video surveillance should be robust and accurate in various situations with low computational costs. This paper presents a new algorithm for abandoned object detection based on the dual background model. Through the template registration of a candidate stationary object and presence authentication methods presented in this paper, we can handle some complex cases such as occlusions, illumination changes, long-term abandonment, and owner's re-attendance as well as general detection of abandoned objects. The proposed algorithm also analyzes video frames at specific intervals rather than consecutive video frames to reduce the computational overhead. For performance evaluation, we experimented with the algorithm using the well-known PETS2006, ABODA datasets, and our video dataset in a live streaming environment, which shows that the proposed algorithm works well in various situations

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“…Deep learning related studies are also in progress. S. Smeureanu et al [20] use a two-stage approach, which detects all stationary objects in the first stage and selects abandoned ones from the detected objects in the second stage. By training the image of the person with an object as a negative image, they consider all objects near the person to be attended.…”

Section: Related Workmentioning

confidence: 99%

Robust Detection of Abandoned Object for Smart Video Surveillance in Illumination Changes

Park

Joo

2019

Sensors

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Most existing abandoned object detection algorithms use foreground information generated from background models. Detection using the background subtraction technique performs well under normal circumstances. However, it has a significant problem where the foreground information is gradually absorbed into the background as time passes and disappears, making it very vulnerable to sudden illumination changes that increase the false alarm rate. This paper presents an algorithm for detecting abandoned objects using a dual background model, which is robust even in illumination changes as well as other complex circumstances like occlusion, long-term abandonment, and owner re-attendance. The proposed algorithm can adapt quickly to various illumination changes. And also, it can precisely track the target objects to determine whether it is abandoned regardless of the existence of foreground information and the effect from the illumination changes, thanks to the largest-contour-based presence authentication mechanism proposed in this paper. For performance evaluation, we trialed the algorithm with the PETS2006, ABODA datasets as well as our dataset, especially to demonstrate its robustness in various illumination changes.

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Real-Time Deep Learning Method for Abandoned Luggage Detection in Video

Cited by 28 publications

References 24 publications

Detection of Abandoned and Stolen Objects Based on Dual Background Model and Mask R-CNN

Detection of Abandoned and Stolen Objects Based on Dual Background Model and Mask R-CNN

Robust Real-time Detection of Abandoned Objects using a Dual Background Model

Robust Detection of Abandoned Object for Smart Video Surveillance in Illumination Changes

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