Robust abandoned object detection using region-level analysis

Pan, Jiyan; Fan, Quanfu; Pankanti, Sharath

doi:10.1109/icip.2011.6116495

Cited by 36 publications

(17 citation statements)

References 9 publications

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“…A similar update process is applied in [35], where the final decisions depend on a stationary object confidence image. The strategy in [76] is able of detecting PSFOs. However, since it is focused in the detection of abandoned objects, it includes a stage to discard these objects from the results.…”

Section: Persistencementioning

confidence: 99%

“…The strategy in [42] uses the coarse-to-fine strategy (a block-based stage followed by a pixel-level stage) proposed in [138]. A hybrid differencing-based strategy at region level is proposed in [76]. The authors of [84] propose using a timeliness BG that uses real world time instead of the frames.…”

Section: Othermentioning

confidence: 99%

“…The authors of [76] use a score function that is updated, at each new frame, from the BG subtraction results. A similar update process is applied in [35], where the final decisions depend on a stationary object confidence image.…”

Section: Persistencementioning

confidence: 99%

See 2 more Smart Citations

Detection of stationary foreground objects: A survey

Cuevas

Martínez

García

2016

Computer Vision and Image Understanding

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Section: Persistencementioning

confidence: 99%

Section: Othermentioning

confidence: 99%

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Detection of stationary foreground objects: A survey

Cuevas

Martínez

García

2016

Computer Vision and Image Understanding

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“…The methods proposed in [7] and [8] involved localizing the static foreground based on the pixels with the maximal accumulated values, which were subsequently considered the candidate regions of stationary objects. However, this category of methods fails in complex scenes.…”

Section: Literaturementioning

confidence: 99%

Abandoned Object Detection using Temporal Consistency Modeling

Patil¹,

Patil²

2017

IJCA

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In this paper an effective approach for detecting the abandoned object/ luggage for video surveillance is present. Here the long-term and short-term background models are combined to extract foreground objects, where each pixel in an input is classified as two bit code. To identify the static foreground regions, a framework is used based on the temporal transition of code pattern and it also determines whether the candidate regions contain the abandoned object by analyzing the back traced trajectories of luggage owner. This paper also introduces the real-time application of proposed method. The real-time application is performed by using raspberry-pi processor and the raspberry-pi camera. The experimental results show that, the proposed approach is effective for detecting abandoned object/ luggage. KeywordsAbandoned object detection, long-term background model, short-term background model, visual surveillance, pixel based finite state machine, image processing.

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“…While the accuracy of this method is good, long computation time makes this approach unsuitable for online analysis. A review of other approaches to stable region detection, with indication of their shortcomings, was given by Pan et al [26]. They proposed a method for selective background model updating based on pixel scores representing their stability.…”

Section: Related Workmentioning

confidence: 99%

Extraction of stable foreground image regions for unattended luggage detection

Szwoch

2014

Multimed Tools Appl

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A novel approach to detection of stationary objects in the video stream is presented. Stationary objects are these separated from the static background, but remaining motionless for a prolonged time. Extraction of stationary objects from images is useful in automatic detection of unattended luggage. The proposed algorithm is based on detection of image regions containing foreground image pixels having stable values in time and checking their correspondence with the detected moving objects. In the first stage of the algorithm, stability of individual pixels belonging to moving objects is tested using a model constructed from vectors. Next, clusters of pixels with stable color and brightness are extracted from the image and related to contours of the detected moving objects. This way, stationary (previously moving) objects are detected. False contours of objects removed from the background are also found and discarded from the analysis. The results of the algorithm may be analyzed further by the classifier, separating luggage from other objects, and the decision system for unattended luggage detection. The main focus of the paper is on the algorithm for extraction of stable image regions. However, a complete framework for unattended luggage detection is also presented in order to show that the proposed approach provides data for successful event detection. The results of experiments in which the proposed algorithm was validated using both standard datasets and video recordings from a real airport security system are presented and discussed.

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Robust abandoned object detection using region-level analysis

Cited by 36 publications

References 9 publications

Detection of stationary foreground objects: A survey

Detection of stationary foreground objects: A survey

Abandoned Object Detection using Temporal Consistency Modeling

Extraction of stable foreground image regions for unattended luggage detection

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