An edge-based method for effective abandoned luggage detection in complex surveillance videos

Dahi, Ilias; Mezouar, Miloud Chikr El; Taleb, Nasreddine; Elbahri, Mohamed

doi:10.1016/j.cviu.2017.01.008

Cited by 22 publications

(9 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Figure 7 shows the sample output of how the abandoned objects have been identified in real time dataset. The performance of this proposed approach has been measured in terms of sensitivity and misclassification rate which are computed as shown in (2) and 3respectively. Table 6 and Table 7 It can be seen from Table 6 that the sensitivity of the proposed approach when tested with ABODA dataset is computed as below.…”

Section: Results Discussionmentioning

confidence: 99%

Computer Vision based System to Detect Abandoned Objects

Balasundaram¹,

Chellappan²

2019

IJEAT

View full text Add to dashboard Cite

Security has become the most essential aspect in every walk of life. With ever growing technology, there has been an apparent inclination towards developing smart surveillance systems that do not stop with just monitoring and recording of events, but also possesses the ability to observe the events and alert in case of any discrepancies if observed. One of the impending threats to security in crowded places is the ignorance of un-attended objects. These un-attended objects or abandoned objects may have been left behind intentionally and may contain hazardous things which can cause huge disasters. This paper is focused towards developing a computer vision based approach that analyses the blob areas to detect any abandoned objects and instantaneously send appropriate alert without any human intervention. The uniqueness of this approach is that it handles the occlusion scenario and also quick execution time to detect abandoned objects. The approach has been tested with various benchmark video datasets and real-time video sequences as well. The performance has been measured in terms of accuracy of classifying abandoned objects in a given video sequence and also the execution time taken for computing the outcome. Results indicate good accuracy in terms of abandoned object detection under varying conditions and scenarios and also faster execution time when compared to other contemporary approaches.

show abstract

Section: Results Discussionmentioning

confidence: 99%

Computer Vision based System to Detect Abandoned Objects

Balasundaram¹,

Chellappan²

2019

IJEAT

View full text Add to dashboard Cite

show abstract

“…Stationary Foreground Detection (SFD) aims to detect objects that remain in place for some time in a video sequence by analyzing spatio-temporal persistent patterns. Existing approaches focus on extracting such patterns from background subtraction approaches [46,47,48] or other features [49,50]. We organize the literature into the following categories (see Table 2 for a summary of the main approaches): single foreground mask, multiple foreground mask, model stability, and other approaches.…”

Section: Stages Of Abandoned Object Detectionmentioning

confidence: 99%

“…Furthermore, this approach was extended to consider color videos by moving from ICA to Independent Vector Analysis (IVA) in mono-camera [92] and multi-camera scenarios [93]. Recently, [50] proposed to overcome the limitations of approaches based on pixel intensities by analyzing persistent foreground edges, which were clustered to obtain a stable edges mask and analyzed in terms of position and stability in time to delineate the object bounding box. Furthermore, an abandoned object classifier based on edges’ position, orientation, and staticness scores was applied.…”

Section: Stages Of Abandoned Object Detectionmentioning

confidence: 99%

“…Depending on the final application, static objects of interest, i.e., candidates of interest, may vary. There are works only focused on detecting abandoned luggage [50,94]; others are geared towards illegally-parked vehicle detection [2,49]; and others adopt a comprehensive approach [60,78]. Since detecting abandoned objects, in general, is the goal of interest, one can make the assumption that whatever is not a person can be considered as an object.…”

Section: Stages Of Abandoned Object Detectionmentioning

confidence: 99%

See 1 more Smart Citation

Abandoned Object Detection in Video-Surveillance: Survey and Comparison

Luna

SanMiguel

Ortego

et al. 2018

Sensors

View full text Add to dashboard Cite

During the last few years, abandoned object detection has emerged as a hot topic in the video-surveillance community. As a consequence, a myriad of systems has been proposed for automatic monitoring of public and private places, while addressing several challenges affecting detection performance. Due to the complexity of these systems, researchers often address independently the different analysis stages such as foreground segmentation, stationary object detection, and abandonment validation. Despite the improvements achieved for each stage, the advances are rarely applied to the full pipeline, and therefore, the impact of each stage of improvement on the overall system performance has not been studied. In this paper, we formalize the framework employed by systems for abandoned object detection and provide an extensive review of state-of-the-art approaches for each stage. We also build a multi-configuration system allowing one to select a range of alternatives for each stage with the objective of determining the combination achieving the best performance. This multi-configuration is made available online to the research community. We perform an extensive evaluation by gathering a heterogeneous dataset from existing data. Such a dataset allows considering multiple and different scenarios, whereas presenting various challenges such as illumination changes, shadows, and a high density of moving objects, unlike existing literature focusing on a few sequences. The experimental results identify the most effective configurations and highlight design choices favoring robustness to errors. Moreover, we validated such an optimal configuration on additional datasets not previously considered. We conclude the paper by discussing open research challenges arising from the experimental comparison.

show abstract

“…Szwoch [18] describes an algorithm for the detection of stable regions by comparing these regions with the contours of moving objects. Dahi et al [14] present a method based on static edge detection and classification. Pham et al [15] propose a two-stage method for unattended object detection.…”

Section: Related Workmentioning

confidence: 99%

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

Smeureanu

Ionescu

2018

2018 26th European Signal Processing Conference (EUSIPCO)

View full text Add to dashboard Cite

Recent terrorist attacks in major cities around the world have brought many casualties among innocent citizens. One potential threat is represented by abandoned luggage items (that could contain bombs or biological warfare) in public areas. In this paper, we describe an approach for real-time automatic detection of abandoned luggage in video captured by surveillance cameras. The approach is comprised of two stages: (i) static object detection based on background subtraction and motion estimation and (ii) abandoned luggage recognition based on a cascade of convolutional neural networks (CNN). To train our neural networks we provide two types of examples: images collected from the Internet and realistic examples generated by imposing various suitcases and bags over the scene's background. We present empirical results demonstrating that our approach yields better performance than a strong CNN baseline method.

show abstract

An edge-based method for effective abandoned luggage detection in complex surveillance videos

Cited by 22 publications

References 9 publications

Computer Vision based System to Detect Abandoned Objects

Computer Vision based System to Detect Abandoned Objects

Abandoned Object Detection in Video-Surveillance: Survey and Comparison

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

Contact Info

Product

Resources

About