Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: , an approach to semantic video analysis that is based on the statistical processing and representation of the motion signal is presented. Overall, the examined video is temporally segmented into shots and for every resulting shot appropriate motion features are extracted; using these, hidden Markov models (HMMs) are employed for performing the association of each shot with one of the semantic classes that are of interest. The novel contributions of this paper lie in the areas of motion information processing and representation. Regarding the motion information processing, the kurtosis of the optical flow motion estimates is calculated for identifying which motion values originate from true motion rather than measurement noise. Additionally, unlike the majority of the approaches of the relevant literature that are mainly limited to global- or camera-level motion representations, a new representation for providing local-level motion information to HMMs is also presented. It focuses only on the pixels where true motion is observed. For the selected pixels, energy distribution-related information, as ll as a complementary set of features that highlight particular spatial attributes of the motion signal, are extracted. Experimental results, as ll as comparative evaluation, from the application of the proposed approach in the domains of  Tennis ,  News  and  Volleyball  broadcast video, and  Human Action  video demonstrate the efficiency of the proposed method.
['Georgios Th. Papadopoulos', 'Alexia Briassouli', 'Vasileios Mezaris', 'Ioannis Kompatsiaris', 'Michael G. Strintzis']
I am writing a research paper on a idea Statistical Motion Information Extraction and Representation for Semantic Video Analysis
[{"from": "human", "value": "I am writing a research paper on a idea Statistical Motion Information Extraction and Representation for Semantic Video Analysis"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: , an approach to semantic video analysis that is based on the statistical processing and representation of the motion signal is presented. Overall, the examined video is temporally segmented into shots and for every resulting shot appropriate motion features are extracted; using these, hidden Markov models (HMMs) are employed for performing the association of each shot with one of the semantic classes that are of interest. The novel contributions of this paper lie in the areas of motion information processing and representation. Regarding the motion information processing, the kurtosis of the optical flow motion estimates is calculated for identifying which motion values originate from true motion rather than measurement noise. Additionally, unlike the majority of the approaches of the relevant literature that are mainly limited to global- or camera-level motion representations, a new representation for providing local-level motion information to HMMs is also presented. It focuses only on the pixels where true motion is observed. For the selected pixels, energy distribution-related information, as ll as a complementary set of features that highlight particular spatial attributes of the motion signal, are extracted. Experimental results, as ll as comparative evaluation, from the application of the proposed approach in the domains of  Tennis ,  News  and  Volleyball  broadcast video, and  Human Action  video demonstrate the efficiency of the proposed method."}]