{"id":{"repo_id":"njit","oai_identifier":"oai:digitalcommons.njit.edu:dissertations-1504"},"canonical_url":"https://search.dev.ndltd.org/etd/njit/oai:digitalcommons.njit.edu:dissertations-1504","repository":{"repo_id":"njit","name":"NJIT","base_url":"https://digitalcommons.njit.edu/do/oai/"},"display":{"title":"Video indexing and summarization using motion activity","abstract":"In this dissertation, video-indexing techniques using low-level motion activity characteristics and their application to video summarization are presented. The MPEG-7 motion activity feature is defined as the subjective level of activity or motion in a video segment. First, a novel psychophysical and analytical framework for automatic measurement of motion activity in compliance with its subjective perception is developed. A psychophysically sound subjective ground truth for motion activity and a test-set of video clips is constructed for this purpose. A number of low-level, compressed domain motion vector based, known and novel descriptors are then described. It is shown that these descriptors successfully estimate the subjective level of motion activity of video clips. Furthermore, the individual strengths and limitations of the proposed descriptors are determined using a novel pair wise comparison framework. It is verified that the intensity of motion activity descriptor of the MPEG-7 standard is one of the best performers, while a novel descriptor proposed in this dissertation performs comparably or better. A new descriptor for the spatial distribution of motion activity in a scene is proposed. This descriptor is supplementary to the intensity of motion activity descriptor. The new descriptor is shown to have comparable query retrieval performance to the current spatial distribution of motion activity descriptor of the MPEG-7 standard. The insights obtained from the motion activity investigation are applied to video summarization. A novel approach to summarizing and skimming through video using motion activity is presented. The approach is based on allocation of playback time to video segments proportional to the motion activity of the segments. Low activity segments are played faster than high activity segments in such a way that a constant level of activity is maintained throughout the video. Since motion activity is a low-complexity descriptor, the proposed summarization techniques are extremely fast. The summarization techniques are successfully used on surveillance video, The proposed techniques can also be used as a preprocessing stage for more complex summarization and content analysis techniques, thus providing significant cost gains.","abstract_html":"In this dissertation, video-indexing techniques using low-level motion activity characteristics and their application to video summarization are presented. The MPEG-7 motion activity feature is defined as the subjective level of activity or motion in a video segment. First, a novel psychophysical and analytical framework for automatic measurement of motion activity in compliance with its subjective perception is developed. A psychophysically sound subjective ground truth for motion activity and a test-set of video clips is constructed for this purpose. A number of low-level, compressed domain motion vector based, known and novel descriptors are then described. It is shown that these descriptors successfully estimate the subjective level of motion activity of video clips. Furthermore, the individual strengths and limitations of the proposed descriptors are determined using a novel pair wise comparison framework. It is verified that the intensity of motion activity descriptor of the MPEG-7 standard is one of the best performers, while a novel descriptor proposed in this dissertation performs comparably or better. A new descriptor for the spatial distribution of motion activity in a scene is proposed. This descriptor is supplementary to the intensity of motion activity descriptor. The new descriptor is shown to have comparable query retrieval performance to the current spatial distribution of motion activity descriptor of the MPEG-7 standard. The insights obtained from the motion activity investigation are applied to video summarization. A novel approach to summarizing and skimming through video using motion activity is presented. The approach is based on allocation of playback time to video segments proportional to the motion activity of the segments. Low activity segments are played faster than high activity segments in such a way that a constant level of activity is maintained throughout the video. Since motion activity is a low-complexity descriptor, the proposed summarization techniques are extremely fast. The summarization techniques are successfully used on surveillance video, The proposed techniques can also be used as a preprocessing stage for more complex summarization and content analysis techniques, thus providing significant cost gains.","abstract_has_math":false,"creators":["Peker, Kadir Askin"],"institution":null,"degree_name":"Doctor of Philosophy in Electrical Engineering - (Ph.D.)","degree_level":null,"degree_discipline":"Electrical and Computer Engineering","degree_department":null,"school":null,"contributors":["Ali N. Akansu","Ajay Divakaran","Nirwan Ansari"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2001,"date_issued":"2001-01-31T08:00:00Z","date_published":"2001-01-31T08:00:00Z","updated_at":"2026-07-24T03:22:45Z","subjects":["Video Indexing","Motion Activity","MPEG-7 standard","Electrical and Electronics"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.njit.edu/dissertations/449","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Ali N. Akansu","Ajay Divakaran","Nirwan Ansari"]},{"key":"dc:creator","label":"Author","values":["Peker, Kadir Askin"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:type","label":"Dc Type","values":["Dissertation"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical and Computer Engineering"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy in Electrical Engineering - (Ph.D.)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Video Indexing","Motion Activity","MPEG-7 standard","Electrical and Electronics"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.njit.edu/dissertations/449"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["In this dissertation, video-indexing techniques using low-level motion activity characteristics and their application to video summarization are presented. The MPEG-7 motion activity feature is defined as the subjective level of activity or motion in a video segment. First, a novel psychophysical and analytical framework for automatic measurement of motion activity in compliance with its subjective perception is developed. A psychophysically sound subjective ground truth for motion activity and a test-set of video clips is constructed for this purpose. A number of low-level, compressed domain motion vector based, known and novel descriptors are then described. It is shown that these descriptors successfully estimate the subjective level of motion activity of video clips. Furthermore, the individual strengths and limitations of the proposed descriptors are determined using a novel pair wise comparison framework. It is verified that the intensity of motion activity descriptor of the MPEG-7 standard is one of the best performers, while a novel descriptor proposed in this dissertation performs comparably or better. A new descriptor for the spatial distribution of motion activity in a scene is proposed. This descriptor is supplementary to the intensity of motion activity descriptor. The new descriptor is shown to have comparable query retrieval performance to the current spatial distribution of motion activity descriptor of the MPEG-7 standard. The insights obtained from the motion activity investigation are applied to video summarization. A novel approach to summarizing and skimming through video using motion activity is presented. The approach is based on allocation of playback time to video segments proportional to the motion activity of the segments. Low activity segments are played faster than high activity segments in such a way that a constant level of activity is maintained throughout the video. Since motion activity is a low-complexity descriptor, the proposed summarization techniques are extremely fast. The summarization techniques are successfully used on surveillance video, The proposed techniques can also be used as a preprocessing stage for more complex summarization and content analysis techniques, thus providing significant cost gains."]},{"key":"dc:title","label":"Title","values":["Video indexing and summarization using motion activity"]}]}],"canonical_facts":{"dc:contributor":["Ali N. Akansu","Ajay Divakaran","Nirwan Ansari"],"dc:creator":["Peker, Kadir Askin"],"dc:description.abstract":["In this dissertation, video-indexing techniques using low-level motion activity characteristics and their application to video summarization are presented. The MPEG-7 motion activity feature is defined as the subjective level of activity or motion in a video segment. First, a novel psychophysical and analytical framework for automatic measurement of motion activity in compliance with its subjective perception is developed. A psychophysically sound subjective ground truth for motion activity and a test-set of video clips is constructed for this purpose. A number of low-level, compressed domain motion vector based, known and novel descriptors are then described. It is shown that these descriptors successfully estimate the subjective level of motion activity of video clips. Furthermore, the individual strengths and limitations of the proposed descriptors are determined using a novel pair wise comparison framework. It is verified that the intensity of motion activity descriptor of the MPEG-7 standard is one of the best performers, while a novel descriptor proposed in this dissertation performs comparably or better. A new descriptor for the spatial distribution of motion activity in a scene is proposed. This descriptor is supplementary to the intensity of motion activity descriptor. The new descriptor is shown to have comparable query retrieval performance to the current spatial distribution of motion activity descriptor of the MPEG-7 standard. The insights obtained from the motion activity investigation are applied to video summarization. A novel approach to summarizing and skimming through video using motion activity is presented. The approach is based on allocation of playback time to video segments proportional to the motion activity of the segments. Low activity segments are played faster than high activity segments in such a way that a constant level of activity is maintained throughout the video. Since motion activity is a low-complexity descriptor, the proposed summarization techniques are extremely fast. The summarization techniques are successfully used on surveillance video, The proposed techniques can also be used as a preprocessing stage for more complex summarization and content analysis techniques, thus providing significant cost gains."],"dc:identifier":["https://digitalcommons.njit.edu/dissertations/449"],"dc:subject":["Video Indexing","Motion Activity","MPEG-7 standard","Electrical and Electronics"],"dc:title":["Video indexing and summarization using motion activity"],"dc:type":["Dissertation"],"thesis:degree_discipline":["Electrical and Computer Engineering"],"thesis:degree_name":["Doctor of Philosophy in Electrical Engineering - (Ph.D.)"]},"updated_at":"2026-07-24T03:22:45Z"}