Superpixels shape analysis for carried object detection
2016 IEEE Winter Applications of Computer Vision Workshops (WACVW)(2016)
摘要
Video surveillance systems generate enormous amounts of data which makes the continuous monitoring of video a very difficult task. Re-identification of subjects in video surveillance systems plays a significant role in public safety. Recent work has focused on appearance modeling and distance learning to establish correspondence between images. However, real-life scenarios suggest that the majority of clothing worn tends to be non-discriminative. Attributes- based re-identification methods try to solve this problem by incorporating semantic attributes which are mid-level features learned a prior. In this paper we present a framework to recognize attributes with applications to carried objects detection. We present a supervised approach based on the contours and shapes of superpixels and histogram of oriented gradients. An experimental evaluation is described using a dataset that was recorded at the Greater Cleveland Regional Transit Authority.
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关键词
superpixel shape analysis,object detection,video surveillance systems,public safety,appearance modeling,distance learning,attribute-based reidentification methods,semantic attributes,Greater Cleveland Regional Transit Authority
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