Visual Sensor Based Abnormal Event Detection with Moving Shadow Removal in Home Healthcare Applications

Sensors

Young-Sook Lee¹ 

[1] Electronic Information Communication Research Center, Pukyong National University, Busan 608-737, Korea; E-Mail:

关键词: abnormal event detection;    visual sensor;    shape features variation;    shadow removal algorithm;    ubiquitous healthcare surveillance;   

DOI  :  10.3390/s120100573

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2012 by the authors; licensee MDPI, Basel, Switzerland