Human Action Recognition in Video

Singh, Dushyant Kumar

doi:10.1007/978-981-13-3140-4_6

Cited by 9 publications

(7 citation statements)

References 12 publications

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“…This research study assesses the proposed approach using metrics such as precision, recall, false positive rate (FPR), and accuracy. 9,15,19,24 Precision and recall here evaluate the model's performance, where precision is the fraction of true positive predictions out of all positive predictions. In contrast, recall is the fraction of true positive predictions out of all actual positive samples.…”

Section: Performance Measuresmentioning

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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Section: Performance Measuresmentioning

confidence: 99%

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

Ansari,

Singh,

Jayaswal

2023

Concurrency and Computation

Self Cite

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show abstract

“…Further, these blocks are categorized into their respective classes. Various handcrafted feature extraction techniques like HOG [ 8 ], SIFT [ 19 ], LBP [ 29 ], etc. are used to evaluate the attributes or features.…”

Section: Literature Reviewmentioning

confidence: 99%

Monitoring social distancing through human detection for preventing/reducing COVID spread

Ansari

Singh

2021

Int. j. inf. tecnol.

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COVID-19 is a severe epidemic that has put the world in a global crisis. Over 42 Million people are infected, and 1.14 Million deaths are reported worldwide as on Oct 23, 2020. A deeper understanding of the epidemic suggests that a person’s negligence can cause widespread harm that would be difficult to negate. Since no vaccine is yet developed, social distancing must be practiced to detain COVID-19 spread. Therefore, we aim to develop a framework that tracks humans for monitoring the social distancing being practiced. To accomplish this objective of social distance monitoring, an algorithm is developed using object detection method. Here, CNN based object detector is explored to detect human presence. The object detector’s output is used for calculating distances between each pair of humans detected. This approach of social distancing algorithm will red mark the persons who are getting closer than a permissible limit. Experimental results prove that CNN based object detectors with our proposed social distancing algorithm exhibit promising outcomes for monitoring social distancing in public areas.

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“…Section 379 of IPC quotes punishment for theft as imprisonment for up to three years, or fine, or both. In recent times, Closed-Circuit TeleVision (CCTV) surveillance infrastructure [6,11] has been adopted by retailers to monitor each buyer in stores/shops. However, real-time analysis of video footage produced by cameras is a tedious task because the human gaze cannot proactively examine each frame continuously, as omissions are usually possible.…”

Section: Introductionmentioning

confidence: 99%

ESAR, An Expert Shoplifting Activity Recognition System

Ansari

Singh

2022

Cybernetics and Information Technologies

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Shoplifting is a troubling and pervasive aspect of consumers, causing great losses to retailers. It is the theft of goods from the stores/shops, usually by hiding the store item either in the pocket or in carrier bag and leaving without any payment. Revenue loss is the most direct financial effect of shoplifting. Therefore, this article introduces an Expert Shoplifting Activity Recognition (ESAR) system to reduce shoplifting incidents in stores/shops. The system being proposed seamlessly examines each frame in video footage and alerts security personnel when shoplifting occurs. It uses dual-stream convolutional neural network to extract appearance and salient motion features in the video sequences. Here, optical flow and gradient components are used to extract salient motion features related to shoplifting movement in the video sequence. Long Short Term Memory (LSTM) based deep learner is modeled to learn the extracted features in the time domain for distinguishing person actions (i.e., normal and shoplifting). Analyzing the model behavior for diverse modeling environments is an added contribution of this paper. A synthesized shoplifting dataset is used here for experimentations. The experimental outcomes show that the proposed approach attains better consequences up to 90.26% detection accuracy compared to the other prevalent approaches.

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Human Action Recognition in Video

Cited by 9 publications

References 12 publications

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

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

Monitoring social distancing through human detection for preventing/reducing COVID spread

ESAR, An Expert Shoplifting Activity Recognition System

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