ESAR, An Expert Shoplifting Activity Recognition System

Ansari, M. A.; Singh, Dushyant Kumar

doi:10.2478/cait-2022-0012

Cited by 12 publications

(13 citation statements)

References 14 publications

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“…A novel system proposed by Ansari et al 25 to identify and mitigate the shoplifting activity in megastore using inception and LSTM framework on self-created dataset which perform 91.8% accuracy. An another approach suggested by Ansari et al 26 to detect shoplifting using optical flow and gradient information, which detect salient motion feature and accurately identify the activity. Dwivedi et al 27 was proposed an novel approach to detect suspicious activity using pretrain network and LSTM on a dataset where activities are collected from eleven benchmark dataset.…”

Section: Literature Surveymentioning

confidence: 99%

Attention-based bidirectional-long short-term memory for abnormal human activity detection

Kumar,

Patel,

Biswas

et al. 2023

Sci Rep

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Abnormal human behavior must be monitored and controlled in today’s technology-driven era, since it may cause damage to society in the form of assault or web-based violence, such as direct harm to a person or the propagation of hate crimes through the internet. Several authors have attempted to address this issue, but no one has yet come up with a solution that is both practical and workable. Recently, deep learning models have become popular as a means of handling massive amounts of data but their potential to categorize the aberrant human activity remains unexplored. Using a convolutional neural network (CNN), a bidirectional long short-term memory (Bi-LSTM), and an attention mechanism to pay attention to the unique spatiotemporal characteristics of raw video streams, a deep-learning approach has been implemented in the proposed framework to detect anomalous human activity. After analyzing the video, our suggested architecture can reliably assign an abnormal human behavior to its designated category. Analytic findings comparing the suggested architecture to state-of-the-art algorithms reveal an accuracy of 98.9%, 96.04%, and 61.04% using the UCF11, UCF50, and subUCF crime datasets, respectively.

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Section: Literature Surveymentioning

confidence: 99%

Attention-based bidirectional-long short-term memory for abnormal human activity detection

Kumar,

Patel,

Biswas

et al. 2023

Sci Rep

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“…Figure 7 represents the comparative analysis between the proposed methodology and existing contemporary activity recognition methods. Some methods 3 , 38 take advantage of CNNs and LSTMs to encode spatial streams for action representations. Other methods 39 encode the action dynamics using a sequential 3DCNN network.…”

Section: Experiments and Analysismentioning

confidence: 99%

“…On the other hand, abnormal or suspicious activities encompass unusual or concerning actions observed in public places, such as fighting, shooting, crowded running, vandalism, shoplifting, robbery, elderly falls, theft, and abandoned luggage that may pose a threat of explosion. 3,4 Hence, there is a crucial need for an automated human activity recognition system capable of identifying abnormal or suspicious activities in public places and buildings to prevent crimes proactively. The proposed system focuses on recognizing suspicious activities such as robbery, fighting, assault, shooting, as well as normal activities.…”

Section: Introductionmentioning

confidence: 99%

“…Normal activities include common actions performed in public places, such as walking, jogging, and clapping. On the other hand, abnormal or suspicious activities encompass unusual or concerning actions observed in public places, such as fighting, shooting, crowded running, vandalism, shoplifting, robbery, elderly falls, theft, and abandoned luggage that may pose a threat of explosion 3 , 4 . Hence, there is a crucial need for an automated human activity recognition system capable of identifying abnormal or suspicious activities in public places and buildings to prevent crimes proactively.…”

Section: Introductionmentioning

confidence: 99%

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Two stream deep neural network based framework to detect abnormal human activities

Kurchaniya

Kumar

2023

J. Electron. Imag.

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In recent times, the volume of video surveillance data has been rapidly increasing, and processing is required to detect various kinds of activities in these data. Most human activity recognition methods primarily focus on detecting normal human activities; however, identifying and detecting suspicious activities is equally important. We propose a two-stream framework for detecting abnormal human activities in real-time videos. The proposed method uses the first stream to extract features related to spatial characteristics and the other stream to extract the temporal features from the videos. The spatial features focus on the contextual details in the frame, including color, texture, and other robust features. The temporal features focus on the features related to the changes in frames with respect to the time, which play a vital role in the detection of activity. The proposed framework leverages the Xception model to extract the features in the spatial and temporal streams. The extracted features are integrated for further processing and detection. To process the integrated features and detect the activity, we train a multi-layer bidirectional long short term memory network using the extracted features. The proposed framework is trained in an end-to-end manner to accurately estimate human activity by learning patterns and forming correlations. The effectiveness of the proposed approach is evaluated by assessing the popular UCF Crime dataset, which encompasses a variety of criminal activities, and using performance measures such as recall, precision, and accuracy. Experimental results indicate that the proposed approach outperforms other contemporary methods, achieving an accuracy of 86%.

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“…Experiments are performed with the use of a synthesized shoplifting dataset, 23 in which the dataset carries a total of 175 videos distributed into the categories “normal” and “shoplifting.” The length of each clip is approximately 10 s and was recorded at a rate of 30 frames per second. The video clips were captured at a 640px × 480px resolution.…”

Section: Experiments and Explorationsmentioning

confidence: 99%

Using postural data and recurrent learning to monitor shoplifting activities in megastores

Ansari,

Singh,

Jayaswal

2023

Concurrency and Computation

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SummaryRecently, researchers have placed a great deal of emphasis on modeling activity patterns to better understand human behavior. Several approaches have been researched so far to develop automatic human activity recognition systems that infer detailed semantics from visual images, aiming to understand real human behavior patterns. However, there is still a need for a cost effective solution to distinguish human actions in the real‐world environment. With this encouragement, a novel approach is proposed to recognize shoplifting acts by examining the posture evidence of the human being. This approach begins by obtaining the two‐dimensional pose reflecting human's body joints as a skeleton from the recorded frames. Subsequently, a preprocessing step is used to preprocess skeleton data, which can handle the occlusion too. Postural feature generation is then used to extract pertinent features from such preprocessed skeletons. Finally, feature deduction is performed to downsize the derived features to a smaller dimension, and activity classification is performed on such reduced features to identify shoplifting behaviors in real time. A synthetic shoplifting dataset and real store recorded videos are used to conduct the experiments, the findings of which appear more promising than those obtained using other cutting‐edge methods, with an accuracy of 97.36% and 91.66% for synthesized and real store recorded inputs.

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ESAR, An Expert Shoplifting Activity Recognition System

Cited by 12 publications

References 14 publications

Attention-based bidirectional-long short-term memory for abnormal human activity detection

Attention-based bidirectional-long short-term memory for abnormal human activity detection

Two stream deep neural network based framework to detect abnormal human activities

Using postural data and recurrent learning to monitor shoplifting activities in megastores

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