{"_buckets": {"deposit": "c403eb2e-595a-4aad-92c4-05156ad3c600"}, "_deposit": {"id": "21710", "owners": [], "pid": {"revision_id": 0, "type": "depid", "value": "21710"}, "status": "published"}, "_oai": {"id": "oai:nagoya.repo.nii.ac.jp:00021710"}, "item_10_alternative_title_19": {"attribute_name": "\u305d\u306e\u4ed6\u306e\u8a00\u8a9e\u306e\u30bf\u30a4\u30c8\u30eb", "attribute_value_mlt": [{"subitem_alternative_title": "A Study on Spatio-Temporal CoHOG Features for Recognition of Generic Objects in Video"}]}, "item_10_biblio_info_6": {"attribute_name": "\u66f8\u8a8c\u60c5\u5831", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2011-12-18", "bibliographicIssueDateType": "Issued"}, "bibliographicIssueNumber": "353", "bibliographicPageEnd": "6", "bibliographicPageStart": "1", "bibliographicVolumeNumber": "111", "bibliographic_titles": [{"bibliographic_title": "\u96fb\u5b50\u60c5\u5831\u901a\u4fe1\u5b66\u4f1a\u6280\u8853\u7814\u7a76\u5831\u544a. 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Recently, generic object recognition has been actively studied for still images, but almost not for videos. As for the generic object recognition in a video, it is important to use both the shape features and the motion features obtained from multiple frames in the video efficiently. In this paper, we propose spatio-temporal CoHOG (Co-occurrence Histograms of Oriented Gradients) features. This is an extension of CoHOG features that provide a high performance for pedestrian detection and others. The spatio-temporal CoHOG features are co-occurrence histograms of oriented spatio-temporal gradients in local regions in a video. In the recognition, a BoF (Bag of Features) representation and a kernel SVM are employed. We conducted an experiment on 1,000 videos including 10 categories collected from the Web. 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