An Approach for Unattended Object Detection through Contour Formation using Background Subtraction

Dwivedi, Neelam; Singh, Dushyant Kumar; Kushwaha, Dharmender Singh

doi:10.1016/j.procs.2020.04.212

Cited by 14 publications

(6 citation statements)

References 12 publications

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“…𝜒 𝑒 * 𝑓 (6) Where, (𝜒) is the number of pixels present in (𝑌) and (𝑒 * 𝑓) is the size of (𝑌). The maximum and minimum eps values (𝛽 max ) and are used for grouping popular events and rare events, respectively.…”

Section: 𝛽 =mentioning

confidence: 99%

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Rare And Popular Event-Based Co-Located Pattern Recognition in Surveillance Videos Using Max-Min PPI-DBSCAN And GREVNN

Jayaram C V

2024

IJRITCC

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Co-located pattern recognition is the process of identifying the sequence of patterns occurring in surveillance videos. In greater part of the existing works, the detection of rare and popular events for effective co-located pattern recognition is not concentrated. Therefore, this paper presents the automatic discovery of the co-located patterns based on rare and popular events in the video. First, the video is converted to frames, and the keyframes are preprocessed. Then, the foreground and background of the frames are estimated, and the rare and popular events are grouped using Maximum-Minimum Pixel-Per-Inch Density-Based Spatial Clustering of Applications with Noise (Max-MinPPI-DBSCAN). From the grouped image, the object detection and mapping are done, and the patch is extracted from it. Next, the edges are detected and from that, for the moving objects, motion is estimated by the Kullback-Leibler Kalman Filter (KLKF). Also, for non-moving objects, the objects/persons are tracked. From the motion estimated and tracked data, time series features are extracted. Then, the optimal features are selected using the Dung Beetle State Transition Probability Optimizer (DBSTPO). Finally, the co-located pattern is classified using a Generalized Recurrent Extreme Value Neural Network (GREVNN), and the alert message is given to the authorities. Hence, the proposed model selected the features in 53239.44ms and classified the event with 99.0723% accuracy and showed better performance than existing works.

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Section: 𝛽 =mentioning

confidence: 99%

“…Based on color, motion, and shape, the abandoned objects are tracked by analyzing the video frames. The abnormal actions that did not happen in general on VSS are also known as a pattern [6], [14]. Further, the objects are recognized by acquiring the directional pattern features.…”

Section: Introductionmentioning

confidence: 99%

Rare And Popular Event-Based Co-Located Pattern Recognition in Surveillance Videos Using Max-Min PPI-DBSCAN And GREVNN

Jayaram C V

2024

IJRITCC

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“…Nevertheless, the edge detection approach has its applications in other areas of research. Dwivedi et al [9] utilized edge detection techniques, including smoothing, intensity gradient calculation, non-maximum suppression, and hysteresis thresholding, to detect edges in images for the purpose of identifying unattended safety breaches in public spaces such as parks, shopping malls, and transportation hubs. This research aimed to enhance security measures by promptly and accurately identifying potential security threats.…”

Section: B Image Classificationmentioning

confidence: 99%

Utilising Convolutional Neural Networks for Pavement Distress Classification and Detection

Sharif,

Emiola,

Zorto

et al. 2023

2023 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)

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This paper examines deep learning models for accurate and efficient identification and classification of pavement distresses. In it, a variety of related studies conducted on the topic as well as the various identification and classification methods proposed, such as edge detection, machine learning classification informed by statistical feature extraction, artificial neural networks, and real-time object detection systems, are discussed. The study investigates the effect of image processing techniques such as grayscaling, background subtraction, and image resizing on the performance and generalizability of the models. Using convolutional neural networks (CNN) architectures, this paper proposes a model that correctly classifies images into five pavement distress categories, namely fatigue (or alligator), longitudinal, transverse, patches, and craters, with an accuracy rate of 90.4% and a recall rate of 90.1%. The model is contrasted to a current state-of-the-art model based on the You Only Look Once framework as well as a baseline CNN model to demonstrate the impact of the image processing and architecture building techniques discussed on performance. The findings of this paper contribute to the fields of computer vision and infrastructure monitoring by demonstrating the efficacy of convolutional neural networks (CNNs) in image classification and the viability of using CNNbased models to automate pavement condition monitoring.

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“…The most recent advancement in video surveillance technology allows it to automatically identify abandoned objects in public areas and illegally parked cars in traffic monitoring systems [13]. Detection of an abandoned object in video surveillance is challenging and essentials for maintaining safety [14]. Manually processing such huge amount data seems impossible and time consumption, An additional measure for public safety is the automatic identification of abandoned items [15].…”

Section: Introductionmentioning

confidence: 99%

Abandoned Object Detection and Classification Using Deep Embedded Vision

Qasim,

Abbas,

Ali

et al. 2024

IEEE Access

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One indispensable element within security systems deployed at public venues such as airports, bus stops, train stations, and marketplaces is video surveillance. The evolution of more robust and efficient automated technological solutions for video surveillance is imperative. In light of the escalating global threat of terrorist attacks in recent years, any unattended object in public areas is treated as potentially suspicious. Ensuring the protection of individuals in these public spaces necessitates the implementation of safety measures. The intricacies of surveillance recordings introduce challenges when it comes to identifying abandoned or removed objects, owing to factors like occlusion, abrupt lighting changes, and other variables. This paper proposes a novel two-stage method for identifying and locating stationary objects in public settings. The first stage uses a sequential model to capture temporal features and detect potential abandoned objects within the monitored area. When the sequential model detects such an object, it triggers a subsequent phase. The second stage uses YOLOv8l model to precisely locate the detected objects. YOLOv8l is renowned for its ability to accurately pinpoint object locations within the surveillance scene. The proposed method achieves remarkable accuracy rates of 99.20% and 99.70% on combined PETS 2006 and ABODA datasets, respectively, effectively localizing the target object. This achievement not only underscores the model's precision in accurately pinpointing the object's position within the given context but also establishes its superiority over other existing models. By integrating these two stages, our method provides an effective solution for enhancing the detection of abandoned objects in public spaces, contributing to improved security and safety measures.

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An Approach for Unattended Object Detection through Contour Formation using Background Subtraction

Cited by 14 publications

References 12 publications

Rare And Popular Event-Based Co-Located Pattern Recognition in Surveillance Videos Using Max-Min PPI-DBSCAN And GREVNN

Rare And Popular Event-Based Co-Located Pattern Recognition in Surveillance Videos Using Max-Min PPI-DBSCAN And GREVNN

Utilising Convolutional Neural Networks for Pavement Distress Classification and Detection

Abandoned Object Detection and Classification Using Deep Embedded Vision

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