Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Manual video surveillance is highly expensive and inconvenient in continuous monitoring, by a security personnel. So automatic video surveillance and activity recognition is needed. , an activity recognition approach is proposed, the difference image is used to extract the motion information based on Region of Interest (ROI). The experiments are carried out on KTH dataset, considering four activities viz (walking, running, waving and boxing) and izmann dataset, considering four activities viz (walking, running, waving one hand, waving both hands) with Support Vector Machines (SVM) for classification. This approach shows an overall performance of 94.75% using KTH dataset and 92% using izmann dataset to recognize the actions. The performance of the proposed approach is comparable with ll known existing methods.
['J. Arunnehru', 'M. Kalaiselvi Geetha']
I am writing a research paper on a idea Motion Intensity Code for Action Recognition in Video Using PCA and SVM
[{"from": "human", "value": "I am writing a research paper on a idea Motion Intensity Code for Action Recognition in Video Using PCA and SVM"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Manual video surveillance is highly expensive and inconvenient in continuous monitoring, by a security personnel. So automatic video surveillance and activity recognition is needed. , an activity recognition approach is proposed, the difference image is used to extract the motion information based on Region of Interest (ROI). The experiments are carried out on KTH dataset, considering four activities viz (walking, running, waving and boxing) and izmann dataset, considering four activities viz (walking, running, waving one hand, waving both hands) with Support Vector Machines (SVM) for classification. This approach shows an overall performance of 94.75% using KTH dataset and 92% using izmann dataset to recognize the actions. The performance of the proposed approach is comparable with ll known existing methods."}]