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Journal articles on the topic 'Activity detection'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 March 2023

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1

Ghosh, Sumon, Prasham Shah, Aditya Ghadge, and Vaibhav Sanghavi. "Suspicious Activity Detection." International Journal for Research in Applied Science and Engineering Technology 10, no. 11 (November 30, 2022): 113–16. http://dx.doi.org/10.22214/ijraset.2022.47186.

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Abstract: In today's insecure world, video surveillance systems play a significant role in keeping both indoors and outdoors secure. Real-time applications can utilize video surveillance components, such as behavior recognition, understanding and classifying activities as normal or suspicious. People are at risk from suspicious activities when it comes to the potential danger they pose. Detecting criminal activities in urban and suburban areas is necessary to minimize such incidents as criminal activity increases. The early days of surveillance were carried out manually by humans and involved a lot of fatigue, since suspicious activities were rare compared to everyday activities. Various surveillance approaches were introduced with the advent of intelligent surveillance systems. This paper analyzes two cases that could pose a threat to human lives if ignored, namely the detection of gun-related crimes, the detection of abandoned luggage, the detection of human violence, the detection of lock hammering, the theft of wallets, and the tempering of ATMs on surveillance video frames. In these papers they have used a neural network model that is Faster R-CNN and YOLOv3 technique to detect these activities.

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2

Singh, Deepti, and Frank Boland. "Voice activity detection." XRDS: Crossroads, The ACM Magazine for Students 13, no. 4 (June 2007): 7. http://dx.doi.org/10.1145/1315325.1315332.

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3

Freeman, Daniel K., and Ivan Boyd. "Voice activity detection." Journal of the Acoustical Society of America 96, no. 6 (December 1994): 3833. http://dx.doi.org/10.1121/1.410520.

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4

Gour, Mr G. B., and Dr V. Udayashankara. "Voice Activity Detection and Pitch analysis in Pathological Voices." International Journal of Trend in Scientific Research and Development Volume-1, Issue-5 (August 31, 2017): 423–28. http://dx.doi.org/10.31142/ijtsrd2324.

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5

Weiqi, Li, Wang Jianming, Liang Jiayu, Jin Guanghao, and Chung Tae‐Sun. "Online dense activity detection." IET Computer Vision 15, no. 5 (May 21, 2021): 323–33. http://dx.doi.org/10.1049/cvi2.12049.

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6

Azamat Kizi, Khamroeva Sarvinoz. "ANTIBACTERIAL ACTIVITY OF LINUM USITATISSIMUM L. SEEDS AND ACTIVE COMPOUND DETECTION." European International Journal of Multidisciplinary Research and Management Studies 02, no. 04 (April 1, 2022): 263–66. http://dx.doi.org/10.55640/eijmrms-02-04-50.

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In the present article, antibacterial properties of four different extracts from Linum usitatissimum L. seeds were screened against four types of Gram-positive and negative bacteria: Staphylococcus aureus, Bacillus cereus, Klebsiella pneumoniae and Pseudomonas aeruginosa using agar-well diffusion method and comparing their antibacterial activities with the antibiotics Ampicillin, Cefalexin, Chloramphenicol and Tetracycline.

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7

Padmaja, B., V. V. Rama Prasad, and K. V. N. Sunitha. "Machine Learning Approach for Stress Detection using Wireless Physical Activity Tracker." International Journal of Machine Learning and Computing 8, no. 1 (February 2018): 33–38. http://dx.doi.org/10.18178/ijmlc.2018.8.1.659.

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8

Shende, Prof Deepali K., Mr Sidheshwar Madrewar, and Mr Shivam Bhongade Mr Shivam Dugade. "Dementia Patient Activity Monitoring and Fall Detection using IoT for Elderly." International Journal of Trend in Scientific Research and Development Volume-3, Issue-4 (June 30, 2019): 363–67. http://dx.doi.org/10.31142/ijtsrd23656.

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9

Fahn, Chin-Shyurng, Jer Ling, Ming-Yuan Yeh, Po-Yen Huang, and Meng-Luen Wu. "Abnormal Maritime Activity Detection in Satellite Image Sequences Using Trajectory Features." International Journal of Future Computer and Communication 8, no. 1 (March 2019): 29–33. http://dx.doi.org/10.18178/ijfcc.2019.8.1.535.

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10

Avgerinakis, Konstantinos, Alexia Briassouli, and Yiannis Kompatsiaris. "Activity detection using Sequential Statistical Boundary Detection (SSBD)." Computer Vision and Image Understanding 144 (March 2016): 46–61. http://dx.doi.org/10.1016/j.cviu.2015.10.013.

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11

Didwania, Prerna, and Vandana Jagtap. "Anomalous Activity Detection in Videos Using Increment Learning." European Journal of Engineering Research and Science 5, no. 3 (March 17, 2020): 297–300. http://dx.doi.org/10.24018/ejers.2020.5.3.1803.

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Nowadays, there is a rapid growth in the number of video cameras at public and private sector because of the monitoring and security purposes. As video surveillance using Closed Circuit Television (CCTV) is in boom nowadays, it has got more research attention due to increased global security concerns. This rapidly growing data can be used to automatically detect the anomalous activities which are going around in our surrounding. Anomalous activity is something that deviates from its normal nature or something that opposes the normal events. This research mainly focuses on detecting anomalous activities in crowded scenes by using video data. Automatically detecting the anomalous activity without using the handcrafted feature has become the need of the hour. This paper contains a survey of different approaches used for anomaly detection in the past. Different incremental and transfer learning approaches are discussed in this paper and it was found that incremental learning has not been used for video-based anomalous activity detection.

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12

Didwania, Prerna, and Vandana Jagtap. "Anomalous Activity Detection in Videos Using Increment Learning." European Journal of Engineering and Technology Research 5, no. 3 (March 17, 2020): 297–300. http://dx.doi.org/10.24018/ejeng.2020.5.3.1803.

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Nowadays, there is a rapid growth in the number of video cameras at public and private sector because of the monitoring and security purposes. As video surveillance using Closed Circuit Television (CCTV) is in boom nowadays, it has got more research attention due to increased global security concerns. This rapidly growing data can be used to automatically detect the anomalous activities which are going around in our surrounding. Anomalous activity is something that deviates from its normal nature or something that opposes the normal events. This research mainly focuses on detecting anomalous activities in crowded scenes by using video data. Automatically detecting the anomalous activity without using the handcrafted feature has become the need of the hour. This paper contains a survey of different approaches used for anomaly detection in the past. Different incremental and transfer learning approaches are discussed in this paper and it was found that incremental learning has not been used for video-based anomalous activity detection.

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13

McCready, V. Ralph, Sabina Dizdarevic, and Thomas Beyer. "Lesion Detection and Administered Activity." Journal of Nuclear Medicine 61, no. 9 (April 3, 2020): 1406–10. http://dx.doi.org/10.2967/jnumed.120.244020.

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14

Ozer, Sedat, Deborah Silver, Karen Bemis, and Pino Martin. "Activity Detection in Scientific Visualization." IEEE Transactions on Visualization and Computer Graphics 20, no. 3 (March 2014): 377–90. http://dx.doi.org/10.1109/tvcg.2013.117.

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15

Hall, J., M. Gilpin, and R. Russell. "Artefactual detection of amylase activity." Microbiology 140, no. 12 (December 1, 1994): 3191–92. http://dx.doi.org/10.1099/13500872-140-12-3191.

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16

Zhu, Xudong, and Hui Li. "Activity clustering for anomaly detection." International Journal of Intelligent Information and Database Systems 7, no. 5 (2013): 441. http://dx.doi.org/10.1504/ijiids.2013.056389.

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17

Lee, Hyeopwoo, and Dongsuk Yook. "Space-time voice activity detection." IEEE Transactions on Consumer Electronics 55, no. 3 (August 2009): 1471–76. http://dx.doi.org/10.1109/tce.2009.5278015.

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18

Liu, Peng, and Zuo-ying Wang. "Audio-visual voice activity detection." Frontiers of Electrical and Electronic Engineering in China 1, no. 4 (December 2006): 425–30. http://dx.doi.org/10.1007/s11460-006-0081-5.

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19

Bhambri, Pankaj, Sachin Bagga, Dhanuka Priya, Harnoor Singh, and Harleen Kaur Dhiman. "Suspicious Human Activity Detection System." December 2020 2, no. 4 (October 31, 2020): 216–21. http://dx.doi.org/10.36548/jismac.2020.4.005.

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In collaboration with machine learning and artificial intelligence, anomaly detection systems are vastly used in behavioral analysis so that you can help in identity and prediction of prevalence of anomalies. It has applications in enterprise, from intrusion detection to system fitness tracking, and from fraud detection in credit score card transactions to fault detection in running environments. With the growing crime charges and human lack of confidence globally, majority of the countries are adopting precise anomaly detection systems to approach closer to a comfy area. Visualizing the Indian crime index which stands at 42. 38, the adoption of anomaly detection structures is an alarming want of time. Our own cannot be prevented with the aid of CCTV installations. These systems not simplest lead to identification on my own, but their optimized versions can help in prediction of unusual activities as properly.

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20

McLoughlin, Ian Vince. "Super-Audible Voice Activity Detection." IEEE/ACM Transactions on Audio, Speech, and Language Processing 22, no. 9 (September 2014): 1424–33. http://dx.doi.org/10.1109/taslp.2014.2335055.

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21

Huang, Tian-Jun. "Detection of Biliverdin Reductase Activity." Current Protocols in Toxicology 00, no. 1 (May 1999): 9.4.1–9.4.10. http://dx.doi.org/10.1002/0471140856.tx0904s00.

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22

Gouda, Magdy Ibraheem, Mohammad Waheed El-Anwar, Sameh Mohammad Hosny, and Maha Atfy Ali. "Telomerase activity detection in cholesteatoma." Egyptian Journal of Ear, Nose, Throat and Allied Sciences 14, no. 1 (March 2013): 7–10. http://dx.doi.org/10.1016/j.ejenta.2012.12.001.

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23

Bashir, Sulaimon Adebayo, Andrei Petrovski, and Daniel Doolan. "A framework for unsupervised change detection in activity recognition." International Journal of Pervasive Computing and Communications 13, no. 2 (June 5, 2017): 157–75. http://dx.doi.org/10.1108/ijpcc-03-2017-0027.

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Purpose This purpose of this paper is to develop a change detection technique for activity recognition model. The approach aims to detect changes in the initial accuracy of the model after training and when the model is deployed for recognizing new unseen activities without access to the ground truth. The changes between the two sessions may occur because of differences in sensor placement, orientation and user characteristics such as age and gender. However, many of the existing approaches for model adaptation in activity recognition are blind methods because they continuously adapt the recognition model without explicit detection of changes in the model performance. Design/methodology/approach The approach determines the variation between reference activity data belonging to different classes and newly classified unseen data. If there is coherency between the data, it means the model is correctly classifying the instances; otherwise, a significant variation indicates wrong instances are being classified to different classes. Thus, the approach is formulated as a two-level architectural framework comprising of the off-line phase and the online phase. The off-line phase extracts of Shewart Chart change parameters from the training data set. The online phase performs classification of new samples and the detection of the changes in each class of activity present in the data set by using the change parameters computed earlier. Findings The approach is evaluated using a real activity-recognition data set. The results show that there are consistent detections that correlate with the error rate of the model. Originality/value The developed approach does not use ground truth to detect classifier performance degradation. Rather, it uses a data discrimination method and a base classifier to detect the changes by using the parameters computed from the reference data of each class to discriminate outliers in the new data being classified to the same class. The approach is the first, to the best of the authors’ knowledge, that addresses the problem of detecting within-user and cross-user variations that lead to concept drift in activity recognition. The approach is also the first to use statistical process control method for change detection in activity recognition, with a robust integrated framework that seamlessly detects variations in the underlying model performance.

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24

M, Liz George, Dr Arun Thomas, Marsha Mariya Kappan, Judith Tony, and Maria Joy. "Activity Monitoring and Unusual Activity Detection for Elderly Homes." International Journal of Innovative Science and Research Technology 5, no. 7 (August 7, 2020): 999–1003. http://dx.doi.org/10.38124/ijisrt20jul754.

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The number of older people in different countries are constantly increasing. Most of this people prefer to live independently. Falls may lead to serious injuries and may even cause death of people. As a solution to this problem it is essential to develop a fall detection system. The objective of this project is to identify and detect unusual activity for an elderly person. Individuals spend the majority of their time in their home or workplace and many feels that these places are their sanctuaries. The information about the person is stored in a database. So in an emergency situation the neighbor can go through the details of the affected person and he/she can refer all the details about the affected person. A camera is continuously capturing the video of the bedridden person. Machine learning techniques use the information to identify and reason about normal behavior in terms of recognized and forecasted activities. Once the abnormal behavior is identified as a threat, a message is sent to the neighbor or corresponding authorities. In most emergency cases, the elderly patient seek in-patient care, which is very expensive and can be a serious financial burden on the patient if the hospital stay is prolonged, and it won’t be affordable for everyone. The proposed work allows people to remain in their comfortable home environment rather than inexpensive and limited nursing homes or hospitals, ensuring maximum independence to the occupants. Therefore, an affordable and comprehensive healthcare solution with minimal workforce have much importance for longterm health management and population. We make use of Artificial Intelligence, Machine Learning, and computer vision

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25

Dorn, Diana, Ronald Gangnon, Jessica Gorzelitz, David Bell, Kelli Koltyn, and Lisa Cadmus-Bertram. "Validation of Automatic Activity Detection on Wearable Activity Trackers." Medicine & Science in Sports & Exercise 50, no. 5S (May 2018): 296. http://dx.doi.org/10.1249/01.mss.0000536058.82502.47.

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26

Marrington, Andrew, Ibrahim Baggili, George Mohay, and Andrew Clark. "CAT Detect (Computer Activity Timeline Detection): A tool for detecting inconsistency in computer activity timelines." Digital Investigation 8 (August 2011): S52—S61. http://dx.doi.org/10.1016/j.diin.2011.05.007.

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27

Lara-Cueva, Roman A., Andres Sebastian Moreno, Julio C. Larco, and Diego S. Benitez. "Real-Time Seismic Event Detection Using Voice Activity Detection Techniques." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 9, no. 12 (December 2016): 5533–42. http://dx.doi.org/10.1109/jstars.2016.2605061.

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28

Namgook Cho and Eun-Kyoung Kim. "Enhanced voice activity detection using acoustic event detection and classification." IEEE Transactions on Consumer Electronics 57, no. 1 (February 2011): 196–202. http://dx.doi.org/10.1109/tce.2011.5735502.

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29

Tahmasbi, R., and S. Rezaei. "Change Point Detection in GARCH Models for Voice Activity Detection." IEEE Transactions on Audio, Speech, and Language Processing 16, no. 5 (July 2008): 1038–46. http://dx.doi.org/10.1109/tasl.2008.922468.

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30

B, Ravinarayana. "Crime Activity Detection Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (July 31, 2022): 3791–95. http://dx.doi.org/10.22214/ijraset.2022.45860.

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Abstract: This study focuses mostly on criminal activity identification. To solve this issue, we employ the machine learning methodology. A criminal offense is characterized as an act or omission that violates the law and is penalized. Crimes seldom involve a particular place because they can occur anywhere, from small towns to major cities. Using an automated video surveillance system rather than human operators is one strategy to combat this issue. A system like this allows for simultaneous monitoring of numerous screens without sacrificing focus. Intelligent video surveillance is only one of the many fields where understanding human behavior in the actual world has applications.

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31

Kamalov, I. A., and R. S. Kurtasanov. "Detection of malignant neoplasms procoagulant activity." Kazanskiy meditsinskiy zhurnal 97, no. 2 (2016): 212–16. http://dx.doi.org/10.17750/kmj2015-212.

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32

Kamalov, I. A., and R. S. Kurtasanov. "Detection of malignant neoplasms procoagulant activity." Kazan medical journal 97, no. 2 (April 15, 2016): 212–16. http://dx.doi.org/10.17750/kmj2016-212.

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Aim. To determine possibilities of malignant neoplasms procoagulant activity detection by idiopathic thrombosis ultrasound imaging.Methods. 587 patients were examined. 347 patients with malignant neoplasms diagnosed in the outpatient clinic settings of the Tatarstan Regional Clinical Cancer Center (Kazan), were included in the main group. 240 patients, in which cancer pathology was excluded, were included in the control group. The groups were matched on age, sex and frequency of venous thromboembolic complications development risk factors not caused by malignant neoplasms. Both groups underwent clinical examination and ultrasound examination of the inferior vena cava, distal abdominal aorta, iliac arteries and veins, lower extremities arteries and veins.Results. Thrombosis clinical manifestations were detected in 12 patients, including 9 patients of the main group and 3 patients of the control group. In most cases of venous thrombosis in the main group (34 people, 79%) there were no clinical signs of thrombosis and it was detected only by ultrasound examination, which allowed to detect venous thrombus in 43 patients of the main group (12.4%) and in 5 patients of the control group (2.1%; t=3.2, p <0.05).Conclusion. The inferior vena cava system venous thrombosis frequency in patients with malignant neoplasms was significantly higher than that in patients without cancer, which indicates malignant neoplasms high procoagulant activity; ultrasonography has high sensitivity in the idiopathic thrombosis detection in cancer patients, and it should be performed regardless of the presence or absence of venous thromboembolic complications clinical manifestations.

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33

V. ADLIN, VINI. "VOICE ACTIVITY DETECTION TECHNIQUES - A REVIEW." i-manager's Journal on Digital Signal Processing 9, no. 2 (2021): 27. http://dx.doi.org/10.26634/jdp.9.2.14396.

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34

Zhigang, Zhang, and Huang Junqin. "AN ADAPTIVE VOICE ACTIVITY DETECTION ALGORITHM." International Journal on Smart Sensing and Intelligent Systems 8, no. 4 (2015): 2175–94. http://dx.doi.org/10.21307/ijssis-2017-848.

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35

Janiszewski, Thomas John. "Voice activity detection driven noise remediator." Journal of the Acoustical Society of America 103, no. 4 (April 1998): 1701. http://dx.doi.org/10.1121/1.421061.

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36

Jie Yin, Qiang Yang, and J. J. Pan. "Sensor-Based Abnormal Human-Activity Detection." IEEE Transactions on Knowledge and Data Engineering 20, no. 8 (August 2008): 1082–90. http://dx.doi.org/10.1109/tkde.2007.1042.

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37

Estévez, P. A., N. Becerra-Yoma, N. Boric, and J. A. Ramırez. "Genetic programming-based voice activity detection." Electronics Letters 41, no. 20 (2005): 1141. http://dx.doi.org/10.1049/el:20052475.

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38

Skvortsov, D. A., M. E. Zvereva, O. V. Shpanchenko, and O. A. Dontsova. "Assays for Detection of Telomerase Activity." Acta Naturae 3, no. 1 (March 15, 2011): 48–68. http://dx.doi.org/10.32607/20758251-2011-3-1-48-68.

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39

Darragh, M. R., E. L. Schneider, J. Lou, P. J. Phojanakong, C. J. Farady, J. D. Marks, B. C. Hann, and C. S. Craik. "Tumor Detection by Imaging Proteolytic Activity." Cancer Research 70, no. 4 (February 9, 2010): 1505–12. http://dx.doi.org/10.1158/0008-5472.can-09-1640.

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40

NORTON, JAMES C., SHAWN E. HOLT, WOODRING E. WRIGHT, and JERRY W. SHAY. "Enhanced Detection of Human Telomerase Activity." DNA and Cell Biology 17, no. 3 (March 1998): 217–19. http://dx.doi.org/10.1089/dna.1998.17.217.

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41

Chen, Zhilin, Foad Sohrabi, and Wei Yu. "Sparse Activity Detection for Massive Connectivity." IEEE Transactions on Signal Processing 66, no. 7 (April 1, 2018): 1890–904. http://dx.doi.org/10.1109/tsp.2018.2795540.

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42

Skocir, Pavle, Petar Krivic, Matea Tomeljak, Mario Kusek, and Gordan Jezic. "Activity Detection in Smart Home Environment." Procedia Computer Science 96 (2016): 672–81. http://dx.doi.org/10.1016/j.procs.2016.08.249.

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43

Chaudhary, Sarita, Mohd Aamir Khan, and Charul Bhatnagar. "Multiple Anomalous Activity Detection in Videos." Procedia Computer Science 125 (2018): 336–45. http://dx.doi.org/10.1016/j.procs.2017.12.045.

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44

Sfar, H., and A. Bouzeghoub. "Activity Recognition for Anomalous Situations Detection." IRBM 39, no. 6 (December 2018): 400–406. http://dx.doi.org/10.1016/j.irbm.2018.10.012.

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45

Tanyer, S. G., and H. Ozer. "Voice activity detection in nonstationary noise." IEEE Transactions on Speech and Audio Processing 8, no. 4 (July 2000): 478–82. http://dx.doi.org/10.1109/89.848229.

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46

Salem, Anas Ahmed ElAraby. "Estrous activity detection device in mammals." Frontiers in Bioscience E5, no. 3 (2013): 798–808. http://dx.doi.org/10.2741/e660.

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47

Fujita, Katsuya, Masanori Kanazawa, Kosuke Mukumoto, Takahiko Nojima, Shinobu Sato, Hiroki Kondo, Michinori Waki, and Shigeori Takenaka. "Electrochemical detection of DNase I activity." Nucleic Acids Symposium Series 50, no. 1 (November 1, 2006): 307–8. http://dx.doi.org/10.1093/nass/nrl153.

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48

In-Chul Yoo, Hyeontaek Lim, and Dongsuk Yook. "Formant-Based Robust Voice Activity Detection." IEEE/ACM Transactions on Audio, Speech, and Language Processing 23, no. 12 (December 2015): 2238–45. http://dx.doi.org/10.1109/taslp.2015.2476762.

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49

Eryilmaz, Erol. "Voice activity detection system and method." Journal of the Acoustical Society of America 113, no. 4 (2003): 1792. http://dx.doi.org/10.1121/1.1572366.

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50

Piekielska, Katarzyna, Magdalena Gębala, Sławomir Gwiazda, Sabine Müller, and Wolfgang Schuhmann. "Impedimetric Detection of Hairpin Ribozyme Activity." Electroanalysis 23, no. 1 (December 3, 2010): 37–42. http://dx.doi.org/10.1002/elan.201000640.

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