A Machine Learning Approach for Localization of Suspicious Objects using Multiple Cameras

Srinath, Raghunandan; Vrindavanam, Jayavrinda; Vasudev, Varun; Supreeth, S; Raj, Harsh; Kesarwani, Ananya

doi:10.1109/inocon50539.2020.9298364

Cited by 2 publications

(3 citation statements)

References 15 publications

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“…Suspicious activity detection will be a major breakthrough in the video surveillance for behavior identification, action recognition, activity classification, etc. Authors in [27] proved the usability of automated surveillance systems. Surveillance plays an important role in maintaining law and order and in detection of possible threat.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Background subtraction Smoothing filter Finds the detection point of SHA and evalueates the corresponding degree of risk [13] Lighweight deep CNN Spatial filter Efficient classification with high accuracy [14] KLT and Kalman filter Efficient real time tracking [15] 2D pose estimation and CNN Effective response system [16] YOLOv3 Quick processing, accurate detection [17] Multilayer LSTM network Less training data, better cross-view and cross-subject evaluation [18] Background subtraction A thresholding technique Less complexity, high accuracy [19] Naïve Bayes classifier Effective and accurate detection and prediction [20] Deep neural network Gun-crime andabandoned laggage detection [21] ANN High accuracy and robustness [22] Bi-LSTM network Adaptive thresholding Skeleton tracking [23] CNN and RNN Apriori detection, simple, yet powerful [24] CNN and DDBN High accuracy [25] Entropy-coded ant colony system High accuracy [26] Mean shift algorithm Efficient object tracking. Box and image sequence segmentation [27] Faster region-based CNN inception V2 framework SHA detection in public places [28] I-ViSE and deep neural networks Smoothing filter Emphasis is given on ensuring that the photos analyzed have significant content and good quality [29] Siamese neural framework Textual windows across segment Superior prediction perfomance for online and offline classrooms [30] Deep fully connected convonutional and recurrent neural network Precice estimates of the total amount of time spent in classes or activities…”

Section: Motivation and Challengesmentioning

confidence: 99%

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Recognition of Suspicious Human Activity in Video Surveillance: A Review

Gupta

Agarwal

2023

Eng. Technol. Appl. Sci. Res.

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Over the past few years, there has been a noticeable growth in the use of video surveillance systems, frequently functioning as integrated systems that remotely monitor key locations. In order to prevent terrorism, theft, accidents, illegal parking, vandalism, fighting, chain snatching, and crime, human activities can be observed through visual surveillance in sensitive and public places like buses, trains, airports, banks, shopping centers, schools, and colleges. In this paper, a review of the state-of-the-art is provided, showing the overall development of identifying suspicious behavior from surveillance recordings over the past few years. We give a quick overview of the issues and difficulties associated with recognizing suspicious human activity. The purpose of this publication is to give this field's scholars a literature evaluation of several suspicious activity recognition systems along with their general structure.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Motivation and Challengesmentioning

confidence: 99%

Recognition of Suspicious Human Activity in Video Surveillance: A Review

Gupta

Agarwal

2023

Eng. Technol. Appl. Sci. Res.

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“…In order to detect moving objects, the background of the frame is subtracted using the Gaussian Mixture Model (GMM) to segment the foreground [19]. To specify the location of the object, the Region-based method is used in traditional CNN to analyze the object's position [24].…”

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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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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A Machine Learning Approach for Localization of Suspicious Objects using Multiple Cameras

Cited by 2 publications

References 15 publications

Recognition of Suspicious Human Activity in Video Surveillance: A Review

Recognition of Suspicious Human Activity in Video Surveillance: A Review

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

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