{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,4]],"date-time":"2026-10-04T01:43:08Z","timestamp":1791078188908,"version":"4.1.0"},"reference-count":17,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2012,1,5]],"date-time":"2012-01-05T00:00:00Z","timestamp":1325721600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Vision-based abnormal event detection for home healthcare systems can be greatly improved using visual sensor-based techniques able to detect, track and recognize objects in the scene. However, in moving object detection and tracking processes, moving cast shadows can be misclassified as part of objects or moving objects. Shadow removal is an essential step for developing video surveillance systems. The goal of the primary is to design novel computer vision techniques that can extract objects more accurately and discriminate between abnormal and normal activities. To improve the accuracy of object detection and tracking, our proposed shadow removal algorithm is employed. Abnormal event detection based on visual sensor by using shape features variation and 3-D trajectory is presented to overcome the low fall detection rate. The experimental results showed that the success rate of detecting abnormal events was 97% with a false positive rate of 2%. Our proposed algorithm can allow distinguishing diverse fall activities such as forward falls, backward falls, and falling asides from normal activities.<\/jats:p>","DOI":"10.3390\/s120100573","type":"journal-article","created":{"date-parts":[[2012,1,6]],"date-time":"2012-01-06T04:04:03Z","timestamp":1325822643000},"page":"573-584","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["Visual Sensor Based Abnormal Event Detection with Moving Shadow Removal in Home Healthcare Applications"],"prefix":"10.3390","volume":"12","author":[{"given":"Young-Sook","family":"Lee","sequence":"first","affiliation":[{"name":"Electronic Information Communication Research Center, Pukyong National University, Busan 608-737, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0121-855X","authenticated-orcid":false,"given":"Wan-Young","family":"Chung","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Pukyong National University, Busan 608-737, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2012,1,5]]},"reference":[{"key":"ref_1","unstructured":"Available online: http:\/\/www.aoa.gov\/ (accessed on 10 September 2011)."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"732","DOI":"10.1111\/j.1525-1497.2004.30387.x","article-title":"Characteristics and circumstances of falls in a hospital setting: A prospective analysis","volume":"19","author":"Hitcho","year":"2004","journal-title":"J. Gen. Intern. Med"},{"key":"ref_3","unstructured":"Tinetti, M.E., Baker, D.I., Dutcher, J., Vincent, J.E., and Rozett, R.T. (1997). Reducing the Risk of Falls among Older Adults in the Community, Peaceable Kingdom Press."},{"key":"ref_4","unstructured":"Alwan, M., Rajendran, P.J., Kell, S., Mack, D., Dalal, S., Wolfe, M., and Felde, R. (2006, January 24\u201328). A smart and passive floor-vibration based fall detector for elderly. Damascus, Syria."},{"key":"ref_5","unstructured":"Purwar, A., Jeong, D.U., and Chung, W.Y. (2007, January 17\u201320). Activity monitoring from real-time triaxial accelerometer data using sensor network. Seoul, Korea."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Nait-Charif, H., and Mckenna, S. (2004, January 23\u201326). Activity summarisation and fall detection in a supportive home environment. Cambridge, UK.","DOI":"10.1109\/ICPR.2004.1333768"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1109\/MPRV.2004.1316817","article-title":"A smart sensor to detect the falls of the elderly","volume":"3","author":"Sixsmith","year":"2004","journal-title":"IEEE Pervas. Comput"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1258\/1357633054068946","article-title":"An intelligent emergency response system: Preliminary development and testing of automated fall detection","volume":"11","author":"Lee","year":"2005","journal-title":"J. Telemed. Telecare"},{"key":"ref_9","unstructured":"Gonzalez, R.C., and Woods, R.E. (2006). Digital Image Processing, Prentice-Hall, Inc. [3rd ed]."},{"key":"ref_10","unstructured":"Tenne, D., and Singh, T. (2000, January 28\u201330). Optimal design of \u03b1-\u03b2-\u03b3(\u03b3) filters. Chicago, IL, USA."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Chen, Y.T., Linn, Y.C., and Fang, W.H. (2010, January 26\u201329). A hybrid human fall detection scheme. Hong Kong.","DOI":"10.1109\/ICIP.2010.5650127"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.cviu.2008.07.006","article-title":"Linguistic summarization of video for fall detection using voxel person and fuzzy logic","volume":"113","author":"Anderson","year":"2009","journal-title":"Comput. Vision Image Understand"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Shieh, W.Y., and Huang, J.C. (2011). Falling-incident detection and throughput enhancement in a multi-camera video-surveillance system. Med. Eng. Phys, in press.","DOI":"10.1016\/j.medengphy.2011.10.016"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1151","DOI":"10.1109\/JPROC.2002.801448","article-title":"Background and foreground modeling using nonparametric kernel density estimation for visual surveillance","volume":"90","author":"Elgammal","year":"2002","journal-title":"Proc. IEEE"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1109\/34.910878","article-title":"The recognition of human movement using temporal templates","volume":"23","author":"Bobick","year":"2001","journal-title":"IEEE Trans. Patt. Anal. Mach. Intell"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1166\/sl.2008.043","article-title":"A novel video sensor based fall detection of the elderly using DDI and temporal templates","volume":"6","author":"Lee","year":"2008","journal-title":"Sens. Lett"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1337","DOI":"10.1109\/TPAMI.2003.1233909","article-title":"Detecting moving objects, ghosts and shadows in video streams","volume":"25","author":"Cucchiara","year":"2003","journal-title":"IEEE Trans. Patt. Anal. Mach. 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