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Performance evaluation of feature sets for carried object detection in still images

dc.contributor.authorKulkarni, Hrushikesh N., author
dc.contributor.authorBeveridge, J. Ross, advisor
dc.contributor.authorDraper, Bruce A., advisor
dc.contributor.authorPasricha, Sudeep, committee member
dc.contributor.authorAlciatore, David G., committee member
dc.description.abstractHuman activity recognition has gathered a lot of interest. The ability to accurately detect carried objects on human beings will directly help activity recognition. This thesis performs evaluation of four different features for carried object detection. To detect carried objects, image chips in a video are extracted by tracking moving objects using an off the shelf tracker. Pixels with similar colors are grouped together by using a superpixel segmentation algorithm. Features are calculated with respect to every superpixel, encoding information regarding their location in the track chip, shape of the superpixel, pose of the person in the track chip, and appearance of the superpixel. ROC curves are used for analyzing the detection of a superpixel as a carried object using these features individually or in a combination. These ROC curves show that the detection using Shape features as they are calculated have very less information. The location features, though simple to calculate, have a significant usable information. Detection using pose of a person in the track chip and appearance of the superpixel depend largely on the data used for their calculation. Pose detections are more likely to be correct if there are no occlusions, while appearance work better if we have high resolution of input images.
dc.format.mediumborn digital
dc.format.mediummasters theses
dc.publisherColorado State University. Libraries
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see
dc.subjectcarried object detection
dc.subjectcomputer vision
dc.subjectimage processing
dc.subjectmachine learning
dc.subjectperformance evaluation of features
dc.subjectvideo surveillance
dc.titlePerformance evaluation of feature sets for carried object detection in still images
dcterms.rights.dplaThis Item is protected by copyright and/or related rights ( You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). and Computer Engineering State University of Science (M.S.)


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Performance evaluation of feature sets for carried object detection in still images