|Hotspot: Making Computer Vision More Effective for Human Video Surveillance|
|Cuong Nguyen, Wu-chi Feng, and Feng Liu|
|Computer Science Department, Portland State University|
Studies have shown that the human capability of monitoring multiple surveillance videos is limited. Computer vision
techniques have been developed to detect abnormal events to support human video surveillance; however, their
results are often unreliable, thus distracting surveillance operators and making them miss important events. This
paper presents Hotspot as a surveillance video visualization system that can effectively leverage noisy computer vision
techniques to support human video surveillance. Hotspot consists of two views: a designated focus view to summarize
videos with detected events and a video-bank view surrounding the focus view to display source surveillance videos.
The focus view allows an operator to quickly dismiss false alarms and focus on true alarms. The video-bank view allows
for extended human video analysis after an important event is detected. Hotspot further provides visual links to assist
quick attention switch from the focus view to the video-bank view . Our experiments show that Hotspot can effectively
integrate noisy, automatic computer vision detection results and better support human video surveillance tasks than the
baseline video surveillance with no or only basic computer vision support.
|Cuong Nguyen, Wu-chi Feng, and
Feng Liu. Hotspot: Making Computer Vision More Effective for Human Video Surveillance.
Information Visualization, 2016. PDF
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ACM CHI 2016. PDF
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ACM CHI 2015. PDF (Best Paper Honorable Mention Award)
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ACM CHI 2013. PDF (Best Paper Honorable Mention Award)
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ACM CHI 2012. PDF
We thank the reviewers of this work for their helpful suggestions. Figure 1 uses an image from Wikipedia under public domain. Figure 5 uses free images from Sithjester. This video was narrated by Hugh Owens. This work was supported by NSF IIS-1321119 and CNS-1218589.