Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Gait recognition is used to identify individuals in image sequences by the way they walk. Nearly all of the approaches proposed for gait recognition are 2D methods based on analyzing image sequences captured by a single camera. , video sequences captured by multiple cameras are used as input, and then a human 3D model is set up. The motion is tracked by applying a local optimization algorithm. The lengths of key segments are extracted as static parameters, and the motion trajectories of lor limbs are used as dynamic features. Finally, linear time normalization is exploited for matching and recognition. The proposed method based on 3D tracking and recognition is robust to the changes of viewpoints. Moreover, better results are achieved for sequences containing difficult surface variations than with 2D methods, which prove the efficiency of our algorithm.
['Guoying Zhao', 'Guoyi Liu', 'Hua Li', 'Matti Pietikäinen']
I am writing a research paper on a idea 3D gait recognition using multiple cameras
[{"from": "human", "value": "I am writing a research paper on a idea 3D gait recognition using multiple cameras"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Gait recognition is used to identify individuals in image sequences by the way they walk. Nearly all of the approaches proposed for gait recognition are 2D methods based on analyzing image sequences captured by a single camera. , video sequences captured by multiple cameras are used as input, and then a human 3D model is set up. The motion is tracked by applying a local optimization algorithm. The lengths of key segments are extracted as static parameters, and the motion trajectories of lor limbs are used as dynamic features. Finally, linear time normalization is exploited for matching and recognition. The proposed method based on 3D tracking and recognition is robust to the changes of viewpoints. Moreover, better results are achieved for sequences containing difficult surface variations than with 2D methods, which prove the efficiency of our algorithm."}]